Reproduction signal evaluation method, reproduction signal evaluation unit, and optical disk device adopting the same

ABSTRACT

A reproduction signal evaluation unit has: a pattern detection section for extracting, from a binary signal, a specific state transition pattern which has a possibility of causing a bit error; a differential metric computing section for computing a differential metric based on the binary signal of the extracted state transition pattern; an error computing section for computing an error rate predicted based on an integration value that is integrated by an integration section, a count value that is counted by a pattern count section, an integration value that is integrated by another integration section, and a count value that is counted by another pattern count section, and a standard deviation computing section for computing a standard deviation based on the computed error rate.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a reproduction signal evaluation methodand reproduction signal evaluation unit using a PRML signal processingsystem, and an optical disk device adopting the same.

2. Description of the Background Art

Recently the shortest mark length of recording marks have reached thelimit for optical resolution, and an increase in inter-symbolinterference and deterioration of SNR (Signal Noise Ratio) are becomingobvious as the density of optical disk media increases, therefore theuse of a PRML (Partial Response Maximum Likelihood) system as a signalprocessing method is becoming common.

The PRML system is a technology combining partial response (PR) andmaximum likelihood (ML) decoding, and is a known system for selecting amost likely signal sequence from a reproduced waveform, assuming theoccurrence of inter-symbol interference. As a result, it is known thatdecoding performance improves compared with a conventional leveldecision system. (e.g. see Blue-ray Disk Books, supervised by HiroshiOgawa and Shinichi Tanaka, Ohmsha Ltd, Dec. 10, 2006)

On the other hand, a shift in signal processing systems from a leveldecision to PRML has resulted in generating some problems inreproduction signal evaluation methods. Jitter, that is a reproductionsignal evaluation index, which has been used conventionally, is based onthe assumption that signals are processed using a level decision system.This means that in some cases jitter has no correlation with thedecoding performance of the PRML system, of which signal processingalgorithms are different from the level decision system. Therefore a newindex having correlation with the decoding performance of the PRMLsystem has been proposed (e.g. see Japanese Patent Application Laid-OpenNo. 2003-141823 and Japanese Patent Application Laid-Open No.2004-213862).

A new index to position shift (edge shift) between a mark and a space,which is critical for recording quality of an optical disk, has alsobeen proposed (e.g. see Japanese Patent Application Laid-Open No.2004-335079). If a PRML system is used, this index must also becorrelated with the performance of the PRML, and must quantitativelyexpress the shift direction and quantity of the edge for each pattern,according to the concept of the PRML system.

As the density of magnetic disk media increases further, the problem ofinter-symbol interference and SNR deterioration becomes more serious. Inthis case, the system margin can be maintained by using a higher levelPRML system (e.g. see Blue-ray Disk Books, supervised by Hiroshi Ogawaand Shinichi Tanaka, Ohmsha Ltd, Dec. 10, 2006). In the case of anoptical disk medium of which diameter is 12 cm and recording capacityper recording layer is 25 GB. The system margin can be maintained byusing a PR1221 mL system, but in the case of a 33.3 GB recordingcapacity per recording layer, a PR12221 ML system must be used. In thisway, it is expected that the tendency to use a higher level PRML systemwould continue in proportion to the increase in densities of opticaldisk media.

Japanese Patent Application Laid-Open No. 2003-141823 and No.2004-213862 disclose using “a differential metric, which is a differenceof the reproduction signals between the most likely first statetransition sequence and the second most likely second state transitionsequence” as the index value.

If there are a plurality of patterns of “a most likely first statetransition sequence and second most likely second state transitionsequence” which have the possibility of causing an error, these patternsmust be statistically processed systematically. This processing methodis not disclosed in Japanese Patent Laid-Open No. 2003-141823 and No.2004-213862. Japanese Patent Application Laid-Open No. 2003-272304discloses a method for detecting a plurality of patterns of “adifferential metric of reproduction signals between the most likelyfirst state transition sequence and the second most likely second statetransition sequence” detected in the same manner as in Japanese PatentApplication Laid-Open No. 2003-141823 and No. 2004-213862, and theprocessing of a pattern group.

In PR12221 ML signal processing, which is disclosed in Japanese PatentApplication Laid-Open No. 2003-272304, there are three types of patternswhich easily cause an error (pattern group of merging paths of whichEuclidian distance is relatively short). In this pattern group. Thepattern generation probability and the number of errors, when thepattern generates errors occur in a pattern, differ depending on thepattern, so according to Japanese Patent Application Laid-Open No.2003-272304, a standard deviation a is determined from the distributionof the index values, which are acquired for each pattern, and the errorsto be generated are predicted based on the generation probability of thepattern (generation frequency with respect to all parameters) and thenumber of errors to be generated when the pattern has an error.

In Japanese Patent Application Laid-Open No. 2003-272304, a method forassuming the distribution of the acquired index values as a normaldistribution and predicting a probability for the index value becoming“0” or less based on the standard deviation σ thereof and varianceaverage value μ, that is, a probability of generation of a bit error, isused as an error prediction method. This, however, is a general methodfor predicting error generation probability. The method for calculatingthe predicted error rate according to Japanese Patent ApplicationLaid-Open No. 2003-272304 is characterized in that generationprobability is determined for each pattern, the predicted error rate iscalculated, and this predicted error rate is used as a guideline ofsignal quality.

However, with the method according to Japanese Patent ApplicationLaid-Open No. 2003-272304, the error rate cannot be predicted accuratelyif recording distortion occurs to recording signals. This problembecomes particularly conspicuous when data is recorded by thermalrecording, such as the case of an optical disk, since recordingdistortion tends to be generated by thermal interference. As the densityof optical disk increases, space between recording pits decreases evenmore, and an increase in thermal interference is expected, thereforethis problem will be unavoidable in the future. The problem of thepredicted error rate calculation method according to Japanese PatentApplication Laid-Open No. 2003-272304, which cannot appropriatelyevaluate the signal quality of signals having recording distortion, willnow be described.

FIG. 21 shows an example of frequency distribution of a differentialmetric of a specific pattern, which is used as a signal index inJapanese Patent Application Laid-Open No. 2003-141823 and No.2003-272304. Generally speaking, the spread of the distribution of thedifferential metric is caused by the noise generated in an optical disk.The reproduction noise generated in an optical disk is random, so thisdistribution usually is a normal distribution. And this differentialmetric is defined as a “differential metric of the most likely firststate transition sequence and second most likely second state transitionsequence”, and is a distribution of which center is a square of theEuclidean distance between the most likely first state transitionsequence and the second most likely second state transition sequence ofan ideal signal (hereafter defined as the signal processing threshold).The standard deviation of which center is this signal processingthreshold is the index value defined in Japanese Patent ApplicationLaid-Open No. 2003-141823, No. 2004-213862 and No. 2003-272304. Theprobability of this differential metric becoming 0 or less correspondsto the predicted error rate. This predicted error rate can be determinedusing the inverse function of the cumulative distribution function ofthis normal distribution.

FIG. 21A is a distribution diagram when no substantial distortionoccurred during recording, and FIG. 21B and FIG. 21C show distributiondiagrams in a state where recording edges in the recording pits shifteddue to thermal interference during recording, and recording distortionoccurred. If distortion occurs due to thermal interference, thefrequency distribution of the differential metric of a specific patternbecomes a normal distribution of which center value is shifted. Thisshift of the center position corresponds to the distortion generated bythermal interference. FIG. 21B and FIG. 21C are cases when apredetermined amount of shift occurred in the plus or minus directionfrom the center of the distribution, and an index value to be determinedis the same value for both FIG. 21B and FIG. 21C, and the index valueincreases since the center of the distribution has shifted. An increasein the index value should mean an increase in the probability of errorgeneration, but errors decrease in the case of FIG. 21C.

This is because in the case of FIG. 21B, where the center of thedistribution is shifted to the side closer to “0”, error generationprobability (probability of differential metric becoming 0 or less)increases, but in the case of FIG. 21C, where the center of thedistribution is shifted to the plus side, error generation probabilitydecreases. This reversal phenomena is because an error is generated onlywhen the index value based on the differential metric approaches 0,which is the major difference from the jitter of the time axis, that isthe index value conventionally used for optical disks. In the case of aconventional jitter of the time axis, errors increase regardless theside, plus or minus, to which the center position of the distributionshifts, therefore the above mentioned problem does not occur.

A problem similar to the above also occurs in the case shown in FIG.21D. FIG. 21D is a case when the determined distribution of thedifferential metric is not normal distribution. This occurs when thethermal interference during recording is high, and thermal interferenceis also received from the recording marks before and after “the mostlikely first state transition sequence and second most likely secondstate transition sequence”. The thermal interference amount is differentdepending on the length of the recording marks before and after, and theshift of recording mark positions generates a differential metricdistribution where two normal distributions (distribution 1 anddistribution 2) overlap.

In distribution 2, where there is a shift to the plus side from thesignal processing threshold, error generation probability drops, but theindex value, which is a standard deviation from the signal processingthreshold as the center, increases because of the influence ofdistribution 2. Just like the case of FIG. 21C, error rate alsodecreases when the index value increases. In this way, if the prior artreported in Japanese Patent Application Laid-Open No. 2003-141823 andNo. 2003-272304 is applied to a high recording density optical disk ofwhich thermal interference is high, the correlation of the index valueand error rate worsens.

An idea for solving this problem is disclosed in Japanese PatentApplication Laid-Open No. 2003-51163. This is a method of counting anumber of differential metrics with which the differential metric,acquired from a predetermined pattern group, which becomes smaller thana predetermined threshold (e.g. half of signal processing threshold). Amethod for determining a predicted error rate based on this count valueis also disclosed. In the case of this method, a side closer to 0 of thedifferential metric distribution, that is the side which has apossibility of generating an error, is used for the evaluation target,so the above mentioned problems in Japanese Patent Application Laid-OpenNo. 2003-141823 and No. 2003-272304 do not occur. But a new problem,mentioned herein below, occurs, since a predetermined threshold is usedand a number of differential metrics exceeding this threshold ismeasured. This problem will be described with reference to FIG. 21E.

FIG. 21E shows an example of counting the differential metrics of thedistribution which exceeds the threshold, which is half of the signalprocessing threshold. The differential metrics less than this thresholdare counted, and the ratio of the parameter of pattern generation andthe count value is used as the signal index. If it is assumed that thedistribution of the differential metric is a normal distribution basedon this count value, the probability when the differential metricbecomes smaller than 0 can be determined, and the predicted error ratecan be calculated. FIG. 21F shows an example of frequency distributionwhen the signal quality is good (signal quality with about an 8%jitter). In such a case, the spread of the distribution of thedifferential metric becomes narrow, and the number of differentialmetrics which exceeds the threshold decreases dramatically.

In the case of FIG. 21F, only about 0.2%, out of the differential metricdistribution, can be measured. This means that a wide area must bemeasured in order to increase the accuracy of the measurement, whichincreases the measurement time and diminishes measurement stability.Also if there are defects and scratches generated during manufacture ofthe disks or if there is dust on a disk surface, the differential metricis generated in an area not greater than the threshold due to thisdefect (illustrated in FIG. 21F). In such a case, a number of thedifferential metrics, which exceed the threshold generated in the normaldistribution, cannot be counted correctly. An advantage of conventionaltime axis jitter used for optical disks is that it is not affected bysuch defects, since standard deviation of measured time fluctuation isused and all the measured data is used.

The method disclosed in Japanese Patent Application Laid-Open No.2003-51163, on the other hand, does not have this advantage of theconventional method based on time axis jitter, which is not affected bythe defects, and therefore has a problem when used for the index valuesof optical disks where such defects as scratches and fingerprints easilyoccur. In order to increase the number of differential metrics to bemeasured using the method according to Japanese Patent ApplicationLaid-Open No. 2003-51163, the threshold could be increased, but if thethreshold is increased, another problem occurs, that is the accuracy ofthe predicted error rate drops. In an extreme case, if the threshold isincreased to half of the Euclidean distance, a number of differentialmetrics that exceed the threshold becomes half of the number of measuredsamples, therefore it no longer depends on the spread of distribution,and accurate measurement becomes possible. In this way, in the case ofthe method according to Japanese Patent Application Laid-Open No.2003-51163, the value of the threshold must be adjusted in order tomaintain constant measurement accuracy depending on the quality ofmeasured signals, and such adjustment is possible if the manner of howdistribution spreads is somewhat understood, nonetheless this is a majorproblem for optical disks, where signal quality changes significantly.

Japanese Patent Application Laid-Open No. 2003-51163 and No. 2003-272304also disclose a method of using bER predicted by the differential metricas the index, but if this is used as an index value, compatibility withthe time axis jitter, which has been used as the signal qualityevaluation index of optical disks, is lost, and handling is difficult.

SUMMARY OF THE INVENTION

The present invention aims to solve the above problem and provides asignal processing method and reproduction signal evaluation unit thatallow the quality of reproduction signals from information recordingmedium to be evaluated with high accuracy, and an optical disk deviceadopting the same.

A reproduction signal evaluation method according to an aspect of thepresent invention is a reproduction signal evaluation method forevaluating a quality of a reproduction signal reproduced from aninformation recording medium based on a binary signal generated from thereproduction signal using a PRML signal processing system, the methodhaving: a pattern extraction step of extracting, from the binary signal,a specific state transition pattern which has the possibility of causinga bit error; a differential metric computing step of computing adifferential metric, which is a difference of a first metric between anideal signal of a most likely first state transition sequencecorresponding to the binary signal and the reproduction signal, and asecond metric between an ideal signal of a second most likely secondstate transition sequence corresponding to the binary signal and thereproduction signal, based on the binary signal of the state transitionpattern extracted in the pattern extraction step; a first integrationstep of integrating the differential metric computed in the differentialmetric computing step; a first count step of counting a number of timesof integration processing in the first integration step; a differentialmetric extraction step of extracting the differential metric not greaterthan a predetermined signal processing threshold; a second integrationstep of integrating the differential metric not greater than the signalprocessing threshold, extracted in the differential metric extractionstep; a second count step of counting a number of times of integrationprocessing in the second integration step; an error rate computing stepof computing an error rate that is predicted based on an integrationvalue that is integrated in the first integration step, a count valuethat is counted in the first count step, an integration value that isintegrated in the second integration step, and a count value that iscounted in the second count step; a standard deviation computing step ofcomputing a standard deviation based on the error rate that is computedin the error rate computing step; and an evaluation step of evaluating aquality of the reproduction signal using the standard deviation computedin the standard deviation computing step.

According to the present invention, when the mean value of thedifferential metrics does not match with the code distance of the idealsignal, an error of the standard deviation, which is generated by theshift of the mean value of the differential metrics from the codedistance of the ideal signal, is corrected using the computedintegration value of the differential metrics and the count value of anumber of times of integration processing of the differential metrics,whereby correlation of the error rate and the signal index value isimproved, and the quality of the reproduction signal reproduced from aninformation recording medium can be evaluated at high accuracy.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram depicting a general structure of an opticaldisk device according to one embodiment of the present invention;

FIG. 2 is a block diagram depicting a general structure of an opticaldisk device according to another embodiment of the present invention;

FIG. 3 is a diagram depicting a state transition rule which isdetermined by an RLL (1, 7) recording code and equalization type PR (1,2, 2, 2, 1) according to one embodiment of the present invention;

FIG. 4 is a trellis diagram corresponding to the state transition ruleshown in FIG. 3;

FIG. 5 is a graph depicting a relationship of a sampling time and areproduction level (signal level) on the transition paths in Table 1;

FIG. 6 is a graph depicting a relationship of a sampling time and areproduction level (signal level) on the transition paths in Table 2;

FIG. 7 is a graph depicting a relationship of a sampling time and areproduction level (signal level) on the transition paths in Table 3;

FIG. 8 is a diagram depicting a distribution of the differential metricof the PR (1, 2, 2, 2, 1) ML according to one embodiment of the presentinvention;

FIG. 9 is a diagram depicting a distribution of the differential metricin a Euclidian distance pattern of a PR (1, 2, 2, 2, 1) ML according toone embodiment of the present invention;

FIG. 10 is a diagram depicting a distribution of the differential metricin each Euclidian distance pattern of a PR (1, 2, 2, 2, 1) ML accordingto one embodiment of the present invention;

FIG. 11 is a diagram depicting a distribution of the differential metricof a PR (1, 2, 2, 2, 1) ML according to one embodiment of the presentinvention;

FIG. 12 is a graph depicting a relationship of the signal evaluationindex value and error rate according to one embodiment of the presentinvention;

FIG. 13 is a block diagram depicting a structure of an optical diskdevice according to still another embodiment of the present invention;

FIG. 14 is a block diagram depicting a structure of an optical diskdevice according to still another embodiment of the present invention;

FIG. 15 is a block diagram depicting a structure of an optical diskdevice according to still another embodiment of the present invention;

FIG. 16A is a diagram depicting a distribution of the differentialmetrics according to the third and fourth embodiments;

FIG. 16B is a diagram depicting a distribution of the differentialmetrics according to the fifth embodiment;

FIG. 17A and FIG. 17B are diagrams depicting a standard deviationcomputing method according to the fifth embodiment;

FIG. 18 is a graph depicting a relationship of a variable a₁(a_(x)) andstandard deviation σ₁/2(σ_(x)/2) when the mean value of the differentialmetrics is a variable b₁(b_(x));

FIG. 19 is a block diagram depicting a structure of an optical diskdevice according to still another embodiment of the present invention;

FIG. 20A and FIG. 20B are diagrams depicting a standard deviationcomputing method according to the sixth embodiment;

FIG. 21A is a diagram depicting the distribution of a conventionaldifferential metric;

FIG. 21B is a diagram depicting the distribution of a conventionaldifferential metric;

FIG. 21C is a diagram depicting the distribution of a conventionaldifferential metric;

FIG. 21D is a diagram depicting the distribution of a conventionaldifferential metric;

FIG. 21E is a diagram depicting the distribution of a conventionaldifferential metric; and

FIG. 21F is a diagram depicting the distribution of a conventionaldifferential metric.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS OF THE Invention

Embodiments of the present invention will now be described withreference to the drawings. The following embodiments are examples ofcarrying out the present invention, and do not limit the technical scopeof the present invention.

In a signal evaluation index detection unit of the present embodiment, aPR12221 ML system, which is an example of a PRML system, is used forsignal processing of a reproduction system, and RLL (Run Length Limited)codes, such as an RLL (1, 7) code, are used for the recording codes. APRML system is a signal processing that combines waveform equalizationtechnology for correcting reproduction distortion, which is generatedwhen information is reproduced, and signal processing technology forselecting a most likely data sequence from the reproduction signal whichincludes data errors, by actively utilizing the redundancy of anequalized waveform.

First signal processing by a PR12221 ML system will be described inbrief, with reference to FIG. 3 and FIG. 4.

FIG. 3 is a state transition diagram depicting the state transitionrule, which is determined by the RLL (1, 7) recording codes and thePR12221 ML system. FIG. 3 shows a state transition diagram which isnormally used when PR ML is described. FIG. 4 is a trellis diagram inwhich the state transition diagram shown in FIG. 3 is developed withrespect to the time axis.

“0” or “1” inside the parenthesis in FIG. 3 indicates a signal sequenceon the time axis, and indicates the state of possibility of the statetransition from the respective state to the next time.

In a PR12221 ML system, a number of states of the decoding unit islimited to 10, because of the combination with the RLL (1, 7) code. Anumber of state transition paths in a PR12221 ML system is 16, and anumber of reproduction levels is 9.

In order to describe the state transition rule of a PR12221 ML system,10 states are represented, as shown in the state transition diagram inFIG. 3, where the state S(0, 0, 0, 0) at a certain time is S0, the stateS(0, 0, 0, 1) is S1, the state S(0, 0, 1, 1,) is S2, the state S(0, 1,1, 1) is S3, the state S(1, 1, 1, 1) is S4, the state S(1, 1, 1, 0) isS5, the state S(1, 1, 0, 0) is S6, the state S(1, 0, 0, 0) is S7, thestate S(1, 0, 0, 1) is S8 and the state S(0, 1, 1, 0) is S9. In FIG. 3,“0” or “1” in parenthesis indicates a signal sequence on the time axis,and shows which state a certain state may possibly become in the statetransition the next time.

In the state transition of the PR12221 ML system shown in FIG. 4, thereare an infinite number of state transition sequence patterns(combination of states) in which two state transitions can occur when apredetermined state at a certain time transit to a predetermined stateat another time. However, patterns which have a high possibility ofcausing an error are limited to specific patterns of which discernmentis difficult. By targeting these state transition patterns in particularwhich can easily generate an error, the state transition sequencepatterns in the PR12221 ML system can be listed as shown in Table 1,Table 2 and Table 3.

TABLE 1 SQUARE OF EUCLIDEAN TRANSITION DISTANCE STATE DATA SEQUENCESTATE TRANSITION SEQUENCE PR EQUALIZED BETWEEN TRANSITION (b_(k-i), . .. , b_(k)) k-9 k-8 k-7 k-6 k-5 k-4 k-3 k-2 k-1 k IDEAL VALUE PATHSS0_(k-5)→S6_(k) (0, 0, 0, 0, x, 1, 1, 0, 0) S0 S1 S2 S3 S5 S6 1 3 5 6 5S0 S0 S1 S2 S9 S6 0 1 3 4 4 14 S0_(k-5)→S5_(k) (0, 0, 0, 0, x, 1, 1, 1,0) S0 S1 S2 S3 S4 S5 1 3 5 7 8 S0 S0 S1 S2 S3 S5 0 1 3 5 7 14S0_(k-5)→S4_(k) (0, 0, 0, 0, x, 1, 1, 1, 1) S0 S1 S2 S3 S4 S4 1 3 5 7 8S0 S0 S1 S2 S3 S4 0 1 3 5 7 14 S2_(k-5)→S0_(k) (0, 0, 1, 1, x, 0, 0, 0,0) S2 S3 S5 S6 S7 S0 5 6 5 3 1 S2 S9 S6 S7 S0 S0 4 4 3 1 0 14S2_(k-5)→S1_(k) (0, 0, 1, 1, x, 0, 0, 0, 1) S2 S3 S5 S6 S7 S1 5 6 5 3 2S2 S9 S6 S7 S0 S1 4 4 3 1 1 14 S2_(k-5)→S2_(k) (0, 0, 1, 1, x, 0, 0,1, 1) S2 S3 S5 S6 S8 S2 5 6 5 4 4 S2 S9 S6 S7 S1 S2 4 4 3 2 3 14S3_(k-5)→S0_(k) (0, 1, 1, 1, x, 0, 0, 0, 0) S3 S4 S5 S6 S7 S0 7 7 5 3 1S3 S5 S6 S7 S0 S0 6 5 3 1 0 14 S3_(k-5)→S1_(k) (0, 1, 1, 1, x, 0, 0,0, 1) S3 S4 S5 S6 S7 S1 7 7 5 3 2 S3 S5 S6 S7 S0 S1 6 5 3 1 1 14S3_(k-5)→S2_(k) (0, 1, 1, 1, x, 0, 0, 1, 1) S3 S4 S5 S6 S8 S2 7 7 5 4 4S3 S5 S6 S7 S1 S2 6 5 3 2 3 14 S7_(k-5)→S6_(k) (1, 0, 0, 0, x, 1, 1, 0,0) S7 S1 S2 S3 S5 S6 2 3 5 6 5 S7 S0 S1 S2 S9 S6 1 1 3 4 4 14S7_(k-5)→S5_(k) (1, 0, 0, 0, x, 1, 1, 1, 0) S7 S1 S2 S3 S4 S5 2 3 5 7 7S7 S0 S1 S2 S3 S5 1 1 3 5 6 14 S7_(k-5)→S4_(k) (1, 0, 0, 0, x, 1, 1,1, 1) S7 S1 S2 S3 S4 S4 2 3 5 7 8 S7 S0 S1 S2 S3 S4 1 1 3 5 7 14S6_(k-5)→S6_(k) (1, 1, 0, 0, x, 1, 1, 0, 0) S6 S8 S2 S3 S5 S6 4 4 5 6 5S6 S7 S1 S2 S9 S6 3 2 3 4 4 14 S6_(k-5)→S5_(k) (1, 1, 0, 0, x, 1, 1, 1,0) S6 S8 S2 S3 S4 S5 4 4 5 7 7 S6 S7 S1 S2 S3 S5 3 2 3 5 6 14S6_(k-5)→S4_(k) (1, 1, 0, 0, x, 1, 1, 1, 1) S6 S8 S2 S3 S4 S4 4 4 5 7 8S6 S7 S1 S2 S3 S4 3 2 3 5 7 14 S4_(k-5)→S0_(k) (1, 1, 1, 1, x, 0, 0, 0,0) S4 S4 S5 S6 S7 S0 8 7 5 3 1 S4 S5 S6 S7 S0 S0 7 5 3 1 0 14S4_(k-5)→S1_(k) (1, 1, 1, 1, x, 0, 0, 0, 1) S4 S4 S5 S6 S7 S1 8 7 5 3 2S4 S5 S6 S7 S0 S1 7 5 3 1 1 14 S4_(k-5)→S2_(k) (1, 1, 1, 1, x, 0, 0,1, 1) S4 S4 S5 S6 S8 S2 8 7 5 4 4 S4 S5 S6 S7 S1 S2 7 5 3 2 3 14

TABLE 2 SQUARE OF EUCLIDEAN TRANSITION DISTANCE STATE DATA SEQUENCESTATE TRANSITION SEQUENCE PR EQUALIZED BETWEEN TRANSITION (b_(k-i), . .. , b_(k)) k-9 k-8 k-7 k-6 k-5 k-4 k-3 k-2 k-1 k IDEAL VALUE PATHSS0_(k-7)→S0_(k) (0, 0, 0, 0, x, 1, S0 S1 S2 S9 S6 S7 S0 S0 1 3 4 4 3 1 0!x, 0, 0, 0, 0) S0 S0 S1 S2 S9 S6 S7 S0 0 1 3 4 4 3 1 12 S0_(k-7)→S1_(k)(0, 0, 0, 0, x, 1, S0 S1 S2 S9 S6 S7 S0 S1 1 3 4 4 3 1 1 !x, 0, 0, 0, 1)S0 S0 S1 S2 S9 S6 S7 S1 0 1 3 4 4 3 2 12 S0_(k-7)→S2_(k) (0, 0, 0, 0, x,1, S0 S1 S2 S9 S6 S7 S1 S2 1 3 4 4 3 2 3 !x, 0, 0, 1, 1) S0 S0 S1 S2 S9S6 S8 S2 0 1 3 4 4 4 4 12 S2_(k-7)→S6_(k) (0, 0, 1, 1, x, 0, S2 S3 S5 S6S8 S2 S9 S6 5 6 5 4 4 4 4 !x, 1, 1, 0, 0) S2 S9 S6 S8 S2 S3 S5 S6 4 4 44 5 6 5 12 S2_(k-7)→S5_(k) (0, 0, 1, 1, x, 0, S2 S3 S5 S6 S8 S2 S3 S5 56 5 4 4 5 6 !x, 1, 1, 1, 0) S2 S9 S6 S8 S2 S3 S4 S5 4 4 4 4 5 7 7 12S2_(k-7)→S4_(k) (0, 0, 1, 1, x, 0, S2 S3 S5 S6 S8 S2 S3 S4 5 6 5 4 4 5 7!x, 1, 1, 1, 1) S2 S9 S6 S8 S2 S3 S4 S4 4 4 4 4 5 7 8 12 S3_(k-7)→S6_(k)(0, 1, 1, 1, x, 0, S3 S4 S5 S6 S8 S2 S9 S6 7 7 5 4 4 4 4 !x, 1, 1, 0, 0)S3 S5 S6 S8 S2 S3 S5 S6 6 5 4 4 5 6 5 12 S3_(k-7)→S5_(k) (0, 1, 1, 1, x,0, S3 S4 S5 S6 S8 S2 S3 S5 7 7 5 4 4 5 6 !x, 1, 1, 1, 0) S3 S5 S6 S8 S2S3 S4 S5 6 5 4 4 5 7 7 12 S3_(k-7)→S4_(k) (0, 1, 1, 1, x, 0, S3 S4 S5 S6S8 S2 S3 S4 7 7 5 4 4 5 7 !x, 1, 1, 1, 1) S3 S5 S6 S8 S2 S3 S4 S4 6 5 44 5 7 8 12 S7_(k-7)→S0_(k) (1, 0, 0, 0, x, 1, S7 S1 S2 S9 S6 S7 S0 S0 23 4 4 3 1 0 !x, 0, 0, 0, 0) S7 S0 S1 S2 S9 S6 S7 S0 1 1 3 4 4 3 1 12S7_(k-7)→S1_(k) (1, 0, 0, 0, x, 1, S7 S1 S2 S9 S6 S7 S0 S1 2 3 4 4 3 1 1!x, 0, 0, 0, 1) S7 S0 S1 S2 S9 S6 S7 S1 1 1 3 4 4 3 2 12 S7_(k-7)→S2_(k)(1, 0, 0, 0, x, 1, S7 S1 S2 S9 S6 S7 S1 S2 2 3 4 4 3 2 3 !x, 0, 0, 1, 1)S7 S0 S1 S2 S9 S6 S8 S2 1 1 3 4 4 4 4 12 S6_(k-7)→S0_(k) (1, 1, 0, 0, x,1, S6 S8 S2 S9 S6 S7 S0 S0 4 4 4 4 3 1 0 !x, 0, 0, 0, 0) S6 S7 S1 S2 S9S6 S7 S0 3 2 3 4 4 3 1 12 S6_(k-7)→S1_(k) (1, 1, 0, 0, x, 1, S6 S8 S2 S9S6 S7 S0 S1 4 4 4 4 3 1 1 !x, 0, 0, 0, 1) S6 S7 S1 S2 S9 S6 S7 S1 3 2 34 4 3 2 12 S6_(k-7)→S2_(k) (1, 1, 0, 0, x, 1, S6 S8 S2 S9 S6 S7 S1 S2 44 4 4 3 2 3 !x, 0, 0, 1, 1) S6 S7 S1 S2 S9 S6 S8 S2 3 2 3 4 4 4 4 12S4_(k-7)→S6_(k) (1, 1, 1, 1, x, 0, S4 S4 S5 S6 S8 S2 S9 S6 8 7 5 4 4 4 4!x, 1, 1, 0, 0) S4 S5 S6 S8 S2 S3 S5 S6 7 5 4 4 5 6 5 12 S4_(k-7)→S5_(k)(1, 1, 1, 1, x, 0, S4 S4 S5 S6 S8 S2 S3 S5 8 7 5 4 4 5 6 !x, 1, 1, 1, 0)S4 S5 S6 S8 S2 S3 S4 S5 7 5 4 4 5 7 7 12 S4_(k-7)→S4_(k) (1, 1, 1, 1, x,0, S4 S4 S5 S6 S8 S2 S3 S4 8 7 5 4 4 5 7 !x, 1, 1, 1, 1) S4 S5 S6 S8 S2S3 S4 S4 7 5 4 4 5 7 8 12

TABLE 3 SQUARE OF EUCLIDEAN TRANSITION DISTANCE STATE DATA SEQUENCESTATE TRANSITION SEQUENCE PR EQUALIZED BETWEEN TRANSITION (b_(k-i), . .. , b_(k)) k-9 k-8 k-7 k-6 k-5 k-4 k-3 k-2 k-1 k IDEAL VALUE PATHSS0_(k-9)→S6_(k) (0, 0, 0, 0, x, 1, S0 S1 S2 S9 S6 S8 S2 S3 S5 S6 1 3 4 44 4 5 6 5 !x, 0, x, 1, 1, 0, 0) S0 S0 S1 S2 S9 S6 S8 S2 S9 S6 0 1 3 4 44 4 4 4 12 S0_(k-9)→S5_(k) (0, 0, 0, 0, x, 1, S0 S1 S2 S9 S6 S8 S2 S3 S4S5 1 3 4 4 4 4 5 7 7 !x, 0, x, 1, 1, 0, 1) S0 S0 S1 S2 S9 S6 S8 S2 S3 S50 1 3 4 4 4 4 5 6 12 S0_(k-9)→S4_(k) (0, 0, 0, 0, x, 1, S0 S1 S2 S9 S6S8 S2 S3 S4 S4 1 3 4 4 4 4 5 7 8 !x, 0, x, 1, 1, 1, 1) S0 S0 S1 S2 S9 S6S8 S2 S3 S4 0 1 3 4 4 4 4 5 7 12 S2_(k-7)→S0_(k) (0, 0, 1, 1, x, 0, S2S3 S5 S6 S8 S2 S9 S6 S7 S0 5 6 5 4 4 4 4 3 1 !x, 1, x, 0, 0, 0, 0) S2 S9S6 S8 S2 S9 S6 S7 S0 S0 4 4 4 4 4 4 3 1 0 12 S2_(k-7)→S1_(k) (0, 0, 1,1, x, 0, S2 S3 S5 S6 S8 S2 S9 S6 S7 S1 5 6 5 4 4 4 4 3 2 !x, 1, x, 0, 0,0, 1) S2 S9 S6 S8 S2 S9 S6 S7 S0 S1 4 4 4 4 4 4 3 1 1 12 S2_(k-7)→S2_(k)(0, 0, 1, 1, x, 0, S2 S3 S5 S6 S8 S2 S9 S6 S8 S2 5 6 5 4 4 4 4 4 4 !x,1, x, 0, 0, 1, 1) S2 S9 S6 S8 S2 S9 S6 S7 S1 S2 4 4 4 4 4 4 3 2 3 12S3_(k-5)→S0_(k) (0, 1, 1, 1, x, 0, S3 S4 S5 S6 S8 S2 S9 S6 S7 S0 7 7 5 44 4 4 3 1 !x, 1, x, 0, 0, 0, 0) S3 S5 S6 S8 S2 S9 S6 S7 S0 S0 6 5 4 4 44 3 1 0 12 S3_(k-5)→S1_(k) (0, 1, 1, 1, x, 0, S3 S4 S5 S6 S8 S2 S9 S6 S7S1 7 7 5 4 4 4 4 3 2 !x, 1, x, 0, 0, 0, 1) S3 S5 S6 S8 S2 S9 S6 S7 S0 S16 5 4 4 4 4 3 1 1 12 S3_(k-5)→S2_(k) (0, 1, 1, 1, x, 0, S3 S4 S5 S6 S8S2 S9 S6 S8 S2 7 7 5 4 4 4 4 4 4 !x, 1, x, 0, 0, 1, 1) S3 S5 S6 S8 S2 S9S6 S7 S1 S2 6 5 4 4 4 4 3 2 3 12 S7_(k-5)→S2_(k) (1, 0, 0, 0, x, 1, S7S1 S2 S9 S6 S8 S2 S3 S5 S6 2 3 4 4 4 4 5 6 5 !x, 0, x, 1, 1, 0, 0) S7 S0S1 S2 S9 S6 S8 S2 S9 S6 1 1 3 4 4 4 4 4 4 12 S7_(k-5)→S2_(k) (1, 0, 0,0, x, 1, S7 S1 S2 S9 S6 S8 S2 S3 S4 S5 2 3 4 4 4 4 5 7 7 !x, 0, x, 1, 1,1, 0) S7 S0 S1 S2 S9 S6 S8 S2 S3 S5 1 1 3 4 4 4 4 5 6 12 S7_(k-5)→S2_(k)(1, 0, 0, 0, x, 1, S7 S1 S2 S9 S6 S8 S2 S3 S4 S4 2 3 4 4 4 4 5 7 8 !x,0, x, 1, 1, 1, 1) S7 S0 S1 S2 S9 S6 S8 S2 S3 S4 1 1 3 4 4 4 4 5 7 12S6_(k-5)→S6_(k) (1, 1, 0, 0, x, 1, S6 S8 S2 S9 S6 S8 S2 S3 S5 S6 4 4 4 44 4 5 6 5 !x, 0, x, 1, 1, 0, 0) S6 S7 S1 S2 S9 S6 S8 S2 S9 S6 3 2 3 4 44 4 4 4 12 S6_(k-5)→S5_(k) (1, 1, 0, 0, x, 1, S6 S8 S2 S9 S6 S8 S2 S3 S4S5 4 4 4 4 4 4 5 7 7 !x, 0, x, 1, 1, 1, 0) S6 S7 S1 S2 S9 S6 S8 S2 S3 S53 2 3 4 4 4 4 5 6 12 S6_(k-5)→S4_(k) (1, 1, 0, 0, x, 1, S6 S8 S2 S9 S6S8 S2 S3 S4 S4 4 4 4 4 4 4 5 7 8 !x, 0, x, 1, 1, 1, 1) S6 S7 S1 S2 S9 S6S8 S2 S3 S4 3 2 3 4 4 4 4 5 7 12 S4_(k-5)→S0_(k) (1, 1, 1, 1, x, 0, S4S4 S5 S6 S8 S2 S9 S6 S7 S0 8 7 5 4 4 4 4 3 1 !x, 1, x, 0, 0, 0, 0) S4 S5S6 S8 S2 S9 S6 S7 S0 S0 7 5 4 4 4 4 3 1 0 12 S4_(k-5)→S1_(k) (1, 1, 1,1, x, 0, S4 S4 S5 S6 S8 S2 S9 S6 S7 S1 8 7 5 4 4 4 4 3 2 !x, 1, x, 0, 0,0, 1) S4 S5 S6 S8 S2 S9 S6 S7 S0 S1 7 5 4 4 4 4 3 1 1 12 S4_(k-5)→S2_(k)(1, 1, 1, 1, x, 0, S4 S4 S5 S6 S8 S2 S9 S6 S8 S2 8 7 5 4 4 4 4 4 4 !x,1, x, 0, 0, 1, 1) S4 S5 S6 S8 S2 S9 S6 S7 S1 S2 7 5 4 4 4 4 3 2 3 12

In each of the tables, Table 1 to Table 3, shows a state transition toindicate a locus of states which merged from the start state, twopossible transition data sequences which underwent state transition, twopossible ideal reproduction waveforms which underwent state transition,and a square of the Euclidean distance of the two ideal reproductionwaveforms.

The square of the Euclidean distance indicates as sum of the square ofthe difference of the two ideal reproduction waveforms. When the errorpossibility of the two reproduction waveforms is judged, tworeproduction waveforms can be more easily distinguished if the value ofthe Euclidean distance is long, therefore a judgment mistake occurs lessfrequently. If the value of the Euclidean distance is short, a judgmentmistake may more frequently occur, since it is difficult to distinguishthe two waveforms having an error possibility. In other words, statetransition patterns of which Euclidean distance is long are statetransition patterns where an error does not occur very much, and statetransition patterns when the Euclidean distance is short are statetransition patterns where an error easily occurs.

In each table, the first column shows the state transition(Sm_(k-9)→Sn_(k)) where two state transitions, which easily cause anerror, branch and merge again. The second column shows the transitiondata sequence (b_(k-i), . . . , b_(k)) which generates this statetransition. X in this state data sequence indicates a bit which has higherror generation possibility among this data, and if this statetransition is judged as erred, a number of X (also !X in Table 2 andTable 3) is a number of errors. In other words, X in a transition datasequence can be either “0” or “1”. One of “0” or “1” corresponds to themost likely first state transition sequence, and the other correspondsto the second most likely second state transition sequence. In Table 2and Table 3, !X indicates a bit inversion of X.

As described in detail later, each decoding data sequence (binarysignal) after a Viterbi decoding section executes decoding processing iscompared with the transition data sequences in Table 1 to Table 3 (Xindicates “don't care”), and a most likely first state transitionsequence having high error possibility and a second most likely secondstate transition sequence are extracted. The third column shows thefirst state transition sequence and second state transition sequence.The fourth column shows two ideal reproduction waveforms (PRequalization ideal values) when a respective state transitionscompletes, and the fifth column shows a square of the Euclidean distanceof these two ideal signals (square of Euclidean distance between paths).

Table 1 shows state transition patterns that could take two statetransitions, and is state transition patterns in the case when a squareof the Euclidean distance is “14”. There are 18 types of statetransition sequence patterns in the case when a square of the Euclideandistance is 14. The state transition sequence patterns shown in Table 1correspond to the edge section (switching of a mark and a space) of thewaveforms of an optical disk. In other words, the state transitionsequence pattern shown in Table 1 is a pattern of a 1-bit shift error atthe edge.

FIG. 5 is a graph depicting the relationship of the sampling time and areproduction level (signal level) in the transition paths in Table 1. Inthe graph in FIG. 5, the x-axis indicates a sampling time (each samplingtiming of a recording sequence), and the y-axis indicates a reproductionlevel. As mentioned above, in the case of a PR12221 ML system, there are9 levels of ideal reproduction signal levels (levels 0 to 8).

As an example, the transition paths when transiting from the state S0(k-5) to the state S6

(k) according to the state transition rule shown in FIG. 3 will bedescribed (see Table 1). In this case, one transition path is a casewhen the recording sequence was detected as a transition of “0, 0, 0, 0,1, 1, 1, 0, 0”. If this transition is converted into a recording state,regarding “0” of the reproduction data as a space portion and “1” as amark portion, the recording state is 4T or longer spaces, and 3T marksand 2T or longer spaces. In FIG. 5, the relationship of the samplingtime and the reproduction level (signal level) in this transition pathis shown as a path A waveform.

The other transition path of the state transition paths from the stateS0 (k-5) to the state S6 (k) in the state transition rule in FIG. 5 is acase when the recording sequence is detected as the transition of “0, 0,0, 0, 0, 1, 1, 0, 0”. If “0” of the reproduction data is regarded as aspace portion and “1” as a mark portion, the recording state correspondsto 5T or longer spaces, and 2T marks and 2T or longer spaces. In FIG. 5,the PR equivalent ideal waveform of this path is shown as a path Bwaveform. The state transition pattern of which square of the Euclideandistance is 14 in Table 1 always includes one edge information (zerocross point), which is characteristic thereof.

FIG. 6 is a graph depicting the relationship of the sampling time andthe reproduction level (signal level) in the transition paths in Table2. In the graph in FIG. 6, the x-axis indicates a sampling time (eachsampling time of recording sequence), and the y-axis indicates areproduction level.

Table 2 shows state transition patterns which could take two statetransitions, just like Table 1, and shows the state transition patternsin the case when a square of the Euclidean distance is 12. There are 18types of state transition patterns in the case when a square of theEuclidean distance is 12. The state transition patterns shown in Table 2are patterns having a 2T marks or 2T spaces shift error, that is 2-bitshift error patterns.

In this case, one path of which recording sequence transits as “0, 0, 0,0, 1, 1, 0, 0, 0, 0, 0” is detected, and if “0” of the reproduction datais regarded as a space portion and “1” as a mark portion, the recordingstate corresponds to 4T or longer spaces, and 2T marks and 5T or longerspaces. In FIG. 6, the PR equivalent ideal waveform of this path isshown as a path A waveform.

As an example, the transition paths when transiting from the state S0(k-7) to the state S0 (k) according to the state transition rule shownin FIG. 3 will be described (see Table 2). In this case, one transitionpath is a case when the recording sequence was detected as a transitionof “0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0”. If this transition path isconverted into a recording state, regarding “0” as the reproduction dataas a space portion and “1” as a mark portion, the recording statecorresponds to 4T or longer spaces, and 2T marks and 5T or longerspaces. In FIG. 6, the relationship of the sampling time and thereproduction level (signal level) in this transition path is shown as apath A waveform.

The other transition path, on the other hand, is a case when therecording sequence is detected as the transition of “0, 0, 0, 0, 0, 1,1, 0, 0, 0, 0”. If this transition path is converted into a recordingstate, regarding “0” of the reproduction data as a space portion and “1”as a mark portion, the recording state corresponds to 5T or longerspaces, and 2T marks and 4T or longer spaces. In FIG. 6, therelationship of the sampling time and the reproduction level (signallevel) in this transition path is shown as a path B waveform. The statetransition pattern of which square of the Euclidean distance is 12 inTable 2 always includes two edge information, the rise and fall of 2T,which is characteristic thereof.

FIG. 7 is a graph depicting the relationship of the sampling time andthe reproduction level (signal level) in the transition paths in Table3. In the graph in FIG. 7, the x-axis indicates a sampling time (eachsampling timing of recording sequence), and the y-axis indicates areproduction level.

Table 3 shows state transition sequence patterns which could take twostate transition sequences, just like Table 1 and Table 2, and shows thestate transition sequence patterns in the case when a square of theEuclidean distance is 12. There are 18 types of state transitionsequence patterns in the case when a square of Euclidean distance is 12.The state transition sequence patterns shown in Table 3 are areas where2T marks and 2T spaces continue, that is 3-bit shift error patterns.

As an example, the transition paths when transiting from the state S0(k-9) to the state S6 (k) according to the state transition rule shownin FIG. 3 will be described (see Table 3). In this case, one transitionpath is a case when the recording sequence was detected as a transitionof “0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0”. If this transition path isconverted into a recording state, regarding “0” of the reproduction dataas a space portion and “1” as a mark portion, the recording statecorresponds to 4T or longer spaces, and 2T marks, 2T spaces, 3T marksand 2T or longer spaces. In FIG. 7, the relationship of the samplingtime and the reproduction level (signal level) in this transition pathis shown as a path A waveform.

The other transition path, on the other hand, is a case when therecording sequence is detected as the transition of “0, 0, 0, 0, 0, 1,1, 0, 0, 1, 1, 0, 0”. If this transition path is converted into arecording state, regarding “0” of the reproduction data as a spaceportion and “1” as a mark portion, the recording state corresponds to 5Tor longer spaces, and 2T marks, 2T spaces, 2T marks and 2T or longerspaces. In FIG. 7, the relationship of the sampling time and thereproduction level (signal level) in this transition path is shown as apath B waveform. The state transition sequence pattern of which squareof the Euclidean distance is 12 in Table 3 always includes at leastthree edge information, which is characteristic thereof.

Embodiments of the present invention will now be described.

First Embodiment

An optical disk device having a reproduction signal evaluation unitaccording to one embodiment of the present invention will now bedescribed with reference to the drawings. FIG. 1 is a block diagramdepicting a structure of the optical disk device 200 according to thefirst embodiment.

An information recording medium 1 is an information recording medium foroptically recording/reproducing information, and is an optical diskmedium, for example. The optical disk device 200 is a reproduction unitwhich reproduces information from the installed information recordingmedium 1.

The optical disk device 200 has an optical head section 2, apreamplifier section 3, an AGC (Automatic Gain Controller) section 4, awaveform equalization section 5, an A/D conversion section 6, a PLL(Phase Locked Loop) section 7, a PR equalization section 8, a maximumlikelihood decoding section 9, a signal evaluation index detectionsection (reproduction signal evaluation unit) 100, and an optical diskcontroller section 15.

The optical head section 2 converges laser beams transmitted through anobjective lens onto a recording layer of the information recordingmedium 1, receives the reflected light thereof, and generates analogreproduction signals which indicate information read from theinformation recording medium 1. The preamplifier section 3 amplifies ananalog reproduction signal, which is generated by the optical headsection 2, using a predetermined gain, and outputs it to the AGC section4. A numerical aperture of the objective lens is 0.7 to 0.9, and is morepreferably 0.85. The wavelength of the laser beam is 410 nm or less, andis more preferably 405 nm.

The preamplifier unit 3 amplifies the analog reproduction signal by apredetermined gain, and outputs it to the AGC section 4.

The AGC section 4 amplifies or attenuates the analog reproductionsignal, and outputs it to the waveform equalization section 5 based onthe output from the A/D conversion section 6, so that the analogreproduction signal from the preamplifier section 3 has a predeterminedamplitude.

The waveform equalization section 5 has LPF characteristics to shield ahigh frequency area of the reproduction signal, and HPF characteristicsto shield a low frequency area of the reproduction signal, and shapesthe reproduction waveform according to desired characteristics, andoutputs it to the A/D conversion section 6.

The A/D conversion section 6 samples an analog reproduction signalsynchronizing with a reproduction clock, which is output from the PLLsection 7, converts the analog reproduction signal into a digitalreproduction signal, and outputs it to the PR equalization section 8,and also to the AGC section 4 and the PLL section 7.

The PLL section 7 generates a reproduction clock to synchronize with thereproduction signal after waveform equalization, based on the outputfrom the A/D conversion section 6, and outputs it to the A/D conversionsection 6.

The PR equalization section 8 has a function to change the filtercharacteristics into characteristics of various PR systems. The PRequalization section 8 performs filtering to be the frequencycharacteristics, which is set so that the frequency characteristics ofthe reproduction system become assumed characteristics of the maximumlikelihood decoding section 9 (e.g. PR (1, 2, 2, 2, 1) equalizationcharacteristics), performs PR equalization processing on digitalreproduction signals for suppressing high frequency noises,intentionally adding inter-symbol interference, and outputs the resultsto the maximum likelihood decoding section 9. The PR equalizationsection 8 may have an FIR (Finite Impulse Response) filter structure,for example, so as to adaptively control the tap coefficient using LMS(The Least-Mean Square) algorithm (see Yoji Iikuni, “Adaptive SignalProcessing Algorithms”, Baihukan, July 2000).

The maximum likelihood decoding unit 9 is a Viterbi decoder, forexample, and uses a maximum likelihood decoding system, which estimatesa most likely sequence based on a code rule intentionally attachedaccording to the type of partial response. This maximum likelihooddecoding section 9 decodes a reproducing signal which was PR-equalizedby the PR equalization section 8, and outputs binary data. This binarydata is output to the optical disk controller section 15 in a subsequentstep, as a decoded binary signal, and after execution of predeterminedprocessing, information recorded on the information recording medium 1is reproduced.

Now a structure of the signal evaluation index detection section 100according to the present embodiment will be described. The signalevaluation index detection section 100 has a pattern detection section101, a differential metric computing section 102, a level decisionsection 103, a pattern count section 104, an integration section 105, anerror computing section 116 and a standard deviation computing section120.

A waveform-shaped digital reproduction signal which is output from thePR equalization section 8, and a binary signal which is output from themaximum likelihood decoding section 9, are input to the signalevaluation index detection section 100. In the signal evaluation indexdetection section 100, the binary signal is input to the patterndetection section 101, and the digital reproduction signal is input tothe differential metric computing section 102, and evaluation processingis executed on digital reproduction signals of the information recordingmedium 1.

The pattern detection section 101 has a function to extract specificstate transition patterns, which have the possibility to cause a biterror, from a binary signal. The pattern detection section 101,according to the present embodiment, extracts specific state transitionpatterns (state transition patterns shown in Table 1) of which square ofthe Euclidean distance between an ideal signal of a most likely firststate transition sequence and an ideal signal of a second most likelysecond state transition sequence is 14. In order to implement this, thepattern detection section 101 stores information of the state transitionpatterns shown in Table 1. And the pattern detection section 101compares the transition data sequences in Table 1, and the binary signalwhich is output from the maximum likelihood decoding section 9. As aresult of this comparison, if the binary signal matches the transitiondata sequences in Table 1, this binary signal becomes an extractiontarget, and the most likely first state transition sequence and the mostlikely second state transition sequence, corresponding to this binarysignal, are selected based on the information in Table 1.

Based on a binary signal extracted by the pattern detection section 101,the differential metric computing section 102 computes a “differentialmetric” which is an absolute value of a difference of “a first metricbetween an ideal signal of a most likely first state transition sequencecorresponding to the binary signal (PR equalization ideal value: seeTable 1) and a digital reproduction signal” and “a second metric betweenan ideal signal of a second most likely second state transition sequencecorresponding to the binary signal and a digital reproduction signal”.Here the first metric is a square of the Euclidean distance between theideal signal of the first state transition sequence and the digitalreproduction signal, and the second metric is a square of the Euclideandistance between the ideal signal of the second state transitionsequence and the digital reproduction signal.

The output of the differential metric computing section 102 is input tothe level decision section 103, and is compared with a predeterminedvalue (signal processing threshold). The pattern count section 104counts a number of differential metrics which are less than the signalprocessing threshold. Each count value is reflected in a pattern groupgeneration frequency when an error rate is calculated. The integrationsection 105 integrates the differential metrics which are less than thesignal processing threshold. A mean value of the differential metricswhich are not greater than the signal processing threshold can bedetermined by dividing the integration value determined by theintegration section 105 by the pattern generation count. The errorcomputing section 116 calculates a predicted error rate based on eachintegration value of differential metrics which are not greater than thesignal processing threshold, and the pattern generation count. Thestandard deviation computing section 120 computes the standard deviationcorresponding to this error rate, and determines this standard deviationas the signal index value to evaluate the signal quality. The process bythis signal evaluation index detection section 100 will now be describedin detail.

The reproduction signal reproduced from the information recording medium1 in the PRML processing is output from the maximum likelihood decodingsection 9 as a binary signal, as mentioned above, and is input to thesignal evaluation index detection section 100. When one of thetransition data sequence patterns in Table 1 is detected from thisbinary signal, the PR equalization ideal values of the first statetransition sequence and the second state transition sequence aredetermined. For example, if (0, 0, 0, 0, X, 1, 1, 0, 0) is decoded asthe binary signal in Table 1, (S0, S1, S2, S3, S5, S6) is selected asthe most likely first state transition sequence, and (S0, S0, S1, S2,S9, S6) is selected as the second most likely second state transitionsequence. The PR equalization ideal value corresponding to the firststate transition sequence is (1, 3, 5, 6, 5). And the PR equalizationideal value corresponding to the second state transition sequence is (0,1, 3, 4, 4).

Then the differential metric computing unit 102 determines a firstmetric (Pb₁₄) which is a square of the Euclidean distance between thereproduction signal sequence (digital reproduction signal) and the PRequalization ideal value corresponding to the first state transitionsequence. In the same way, the differential metric computing unit 102determines a second metric (Pa₁₄) which is a square of the Euclideandistance between the reproduction signal sequence and the PRequalization ideal value corresponding to the second state transitionsequence. The differential metric computing unit 102 determines anabsolute value of the difference between the first metric (Pb₁₄) and thesecond metric (Pa₁₄), as the differential metric D₁₄=|Pa₁₄−Pb₁₄|. Thecomputing of Pb₁₄ is shown in Expression (1), and the computing of Pa₁₄is shown in Expression (2). In these expressions, b_(k) denotes the PRequalization ideal value corresponding to the first state transitionsequence, a_(k) denotes a PR equalization ideal value corresponding tothe second state transition sequence, and x_(k) denotes a reproductionsignal sequence.

$\begin{matrix}{{Pb}_{14} = {\sum\limits_{k = {k - 5}}^{k}\left( {x_{k} - b_{k}} \right)^{2}}} & ({E1}) \\{{Pa}_{14} = {\sum\limits_{k = {k - 5}}^{k}\left( {x_{k} - a_{k}} \right)^{2}}} & ({E2}) \\{D_{14} = {{{Pa}_{14} - {Pb}_{14}}}} & ({E3})\end{matrix}$

In FIG. 9, an area above the signal processing threshold is an areawhere error does not occur, and it is therefore unnecessary to predictan error rate. Hence in order to predict an error rate based on thestandard deviation of the differential metric, only an area not greaterthan the signal processing threshold becomes a target. A method forcalculating this error rate will now be described.

The differential metric D₁₄, which is an output from the differentialmetric computing section 102, is input to the level decision section103, and is compared with a predetermined value (signal processingthreshold). In the present embodiment, the signal processing thresholdaccording to an extraction target specific state transition pattern isset to “14”, which is a square of the Euclidean distance between anideal signal of the most likely first state transition sequence and anideal signal of the second most likely second state transition sequence.If the differential metric D₁₄ is not greater than the signal processingthreshold “14”, the level decision section 103 outputs the value of thisdifferential metric D₁₄ to the integration section 105, and the patterncount section 104 counts up the count value. The integration section 105integrates the differential metric cumulatively each time thedifferential metric D₁₄, which is not greater than the signal processingthreshold, is input. Then the error computing section 116 calculates apredetermined error date using an integration value of the differentialmetric not greater than the signal processing threshold and number ofgenerated patterns, counted by the pattern count section 104. Operationof the error computing section 116 will now be described.

The mean value of the differential metrics which are not greater thanthe signal processing threshold can be determined by dividing theintegration value, determined by the integration section 105, by anumber of differential metrics less than the signal processing threshold(number of generated patterns), which was counted up by the patterncount section 104. When it is assumed that the mean value of thedifferential metrics, which are not greater than the signal processingthreshold, is M(x), the mean value of the distribution functions is μ,the standard deviation is σ₁₄, the probability density function is f,and the distribution function is a normal distribution, and the absolutemean value m of the differential metrics, which are less than the signalprocessing threshold, is given by the following Expression (4).

$\begin{matrix}\begin{matrix}{m = {{M(X)}}} \\{= {\int_{- \infty}^{\infty}{{{x - \mu}}{f(x)}{x}}}} \\{= {\frac{1}{\sqrt{2\pi}\sigma_{14}}\begin{Bmatrix}{{- {\int_{- \infty}^{\mu}{\left( {x - \mu} \right)^{- \frac{{({x - \mu})}^{2}}{2\sigma_{14}^{2}}}{x}}}} +} \\{\int_{\mu}^{\infty}{\left( {x - \mu} \right)^{- \frac{{({x - \mu})}^{2}}{2\sigma_{14}^{2}}}{x}}}\end{Bmatrix}}} \\{= {\frac{\sigma_{14}}{\sqrt{2\pi}}\left\{ {{- {\int_{- \infty}^{0}{{te}^{- \frac{t^{2}}{2}}{t}}}} + {\int_{0}^{\infty}{{te}^{- \frac{t^{2}}{2}}{t}}}} \right\}}} \\{= {\frac{\sigma_{14}}{\sqrt{2\pi}}\left( {{- \left\lbrack {- ^{- \frac{t^{2}}{2}}} \right\rbrack_{- \infty}^{0}} + \left\lbrack ^{- \frac{t^{2}}{2}} \right\rbrack_{0}^{\infty}} \right)}} \\{= {\frac{\sigma_{14}}{\sqrt{2\pi}}\left( {{- \left( {- 1} \right)} + 1} \right)}} \\{= {\sqrt{\frac{2}{\pi}}\sigma_{14}}}\end{matrix} & ({E4})\end{matrix}$

Therefore the relationship of the standard deviation σ_(l4) of thedifferential metrics, which are not greater than the signal processingthreshold and the absolute mean value m of the differential metrics,which are not greater than the signal processing threshold, isdetermined by the following Expression (5).

$\begin{matrix}{{m = {{\sqrt{\frac{2}{\pi}}\sigma_{14}} = {0.79788\sigma_{14}}}}{\sigma_{14} = {{\sqrt{\frac{\pi}{2}}m} = {1.25331m}}}} & ({E5})\end{matrix}$

As Expression (4) and Expression (5) show, in order to determine thestandard deviation σ₁₄ of the differential metrics which are not greaterthan the signal processing threshold, the absolute mean value m of thedifferential metrics, which are not greater than the signal processingthreshold, is determined, and is then multiplied by about 1.253. Sincethe signal processing threshold is fixed, the standard deviation σ₁₄ canbe calculated from the absolute mean value m. The probability of errorgeneration (error rate bER₁₄), which is computed by the error computingsection 116, can be determined by the following Expression (6).

$\begin{matrix}{{bER}_{14} = {1 \times p_{14} \times {\int_{- \infty}^{0}{\frac{1}{\sqrt{2\; \pi}\sigma_{14}}^{- \frac{{({x - d_{14}^{2}})}^{2}}{2\sigma_{14}^{2}}}{x}}}}} & ({E6})\end{matrix}$

Here d₁₄ in Expression (6) denotes the Euclidean distance between anideal signal of the most likely first state transition sequence in theextraction target state transition patterns, and an ideal signal of thesecond most likely second state transition sequence. In the case of thepresent embodiment, a square of the Euclidean distance d₁₄ ²=14 is used.Therefore if the standard deviation given by Expression (5), which isdetermined by the integration value and integration count, is σ_(m),then the error rate bER_(14B), predicted based on the computing of theerror computing section 116, is given by the following Expression (7).p₁₄ (=0.4) is an error generation probability in the distributioncomponents with respect to all the channel points. Errors generated inthe state transition sequence patterns in Table 1 are 1-bit errors, sothe error generation probability has been multiplied by 1.

The standard deviation computing section 120 converts this error rate(error generation probability) bER₁₄ into a signal index value M, tomake it to an index which can be handled in a similar manner as ajitter. By using Expression (7), the standard deviation computing unit120 converts bER₁₄ into signal index value M using the standarddeviation a corresponding to the predicted error rate.

$\begin{matrix}{{bER}_{14} = {\frac{p_{14}}{2}{{erfc}\left( \frac{1}{2\sqrt{2}M} \right)}}} & ({E7})\end{matrix}$

Here erfc( ) is an integration value of the complementary errorfunction. If the defining expression of the signal index value Maccording to the present embodiment is the following Expression (8),then the index value M using a virtual standard deviation a can bedetermined by substituting bER₁₄, calculated by Expression (6), inExpression (7).

$\begin{matrix}{M - \frac{\sigma}{2 \cdot d_{14}^{2}}} & ({E8})\end{matrix}$

In the above description, a virtual standard deviation a and signalindex value M are calculated from a predicted error rate usingExpression (6) to Expression (8).

As described above, according to the present embodiment, the statetransition sequence patterns of merging paths of which Euclideandistance in the PRML signal processing is relatively small are targeted,and the signal evaluation index M is generated based on the differentialmetric information of the state transition sequence patterns.Specifically, a predicted error rate is calculated from the mean valueof the differential metric information which is not greater than thesignal processing threshold, then the virtual standard deviation a iscalculated from the error rate, and the signal evaluation index Mincluding this standard deviation a of the normal distribution isgenerated. As a result, it is possible to realize a signal evaluationmethod and evaluation index having very high correlation with the errorrate.

In the case of a conventionally proposed distribution evaluation of asimple differential metric, it is difficult to calculate a signal indexhaving correlation with an error rate, because of the recordingdistortion due to thermal interference generated as a higher density ofan optical disk that will be increasingly demanded in the future. Thepresent embodiment is for solving this problem, and a key point thereofis that only one side of the distribution components of the differentialmetrics, where errors are generated, is targeted to calculate the signalindex which has high correlation with actual errors to be generated, andthe standard deviation a of both virtual sides distribution isdetermined based on this one sided distribution.

According to the present embodiment, for the specific state transitionpattern which may cause a bit error, the pattern detection section 101according to the present embodiment extracts specific state transitionpatterns (state transition patterns shown in Table 1) with which asquare of the Euclidean distance between an ideal signal of the mostlikely first state transition sequence and an ideal signal of the secondmost likely second state transition sequence becomes 14, but the presentinvention is not limited to this. For example, specific state transitionpatterns (state transition patterns shown in Table 2 or Table 3) withwhich this square of the Euclidean distance becomes 12 may be extracted.

The optical disk controller section 15 functions as an evaluationsection, which executes evaluation processing based on the signalevaluation index M received from the standard deviation computingsection 120. This evaluation result can be displayed on a displaysection, which is not illustrated, or stored in a memory as evaluationdata.

In the present embodiment, the optical disk device 200 having the signalevaluation index detection section 100 was described, but the presentinvention may be constructed as an optical disk evaluation unit(reproduction signal evaluation unit) having the optical disk controllersection 15 as an evaluation section. The optical disk evaluation unitcan be used for evaluating the information recording medium beforeshipment, whether this information recording medium 1 has a qualityconforming to a predetermined standard or not.

The optical disk device 200 having the reproduction signal evaluationunit may be arranged to perform the following operation. For example,the quality of the reproduction signal is evaluated for commercialoptical disks (blank disks) shipped from a factory, and optical diskswhich are judged as not satisfying a predetermined quality are rejected.It is for certain possible that optical disks recorded by a recorder(recording using a device other than this optical disk device) can beevaluated by this optical disk device and the optical disk, which arejudged as not satisfying a predetermined quality, and are rejected.

If the optical disk device 200 can record and reproduce information,then evaluation based on test recording can be performed beforerecording information on the optical disk. In this case, the quality ofreproduction signals is evaluated for the test information recorded bythe optical disk device 200, and if NG, recording conditions areadjusted until NG becomes OK, and the optical disk is rejected if NGcontinues after a predetermined number of times of adjustment.

Second Embodiment

An optical disk device having a reproduction signal evaluation unitaccording to another embodiment of the present invention will now bedescribed with reference to the drawings. A composing element the sameas the first embodiment is denoted with a same element number, for whichdescription can be omitted. FIG. 2 is a block diagram depicting thestructure of the optical disk device 400 of second embodiment.

An information recording medium 1 is an information recording medium foroptically recording/reproducing information, and is an optical diskmedium, for example. The optical disk device 400 is a reproduction unitwhich reproduces information from the installed information recordingmedium 1.

The optical disk device 400 has an optical head section 2, apre-amplifier section 3, an AGC (Automatic Gain Controller) section 4, awaveform equalization section 5, an A/D conversion section 6, a PLL(Phase Locked Loop) section 7, a PR equalization section 8, a maximumlikelihood decoding section 9, a signal evaluation index detectionsection (reproduction signal evaluation unit) 300 and an optical diskcontroller section 15. The structures and functions of these elementsconstituting the optical disk device 400 are the same as the firstembodiment, and descriptions thereof are omitted here.

Now a structure of the signal evaluation index detection section 300according to the present embodiment will be described. The signalevaluation index detection section 300, just like the signal evaluationindex detection section 100 of the first embodiment, can be used as anevaluation unit for judging whether the quality of the informationrecording medium 1 conforms to a predetermined standard before shipment.The present signal evaluation index detection section 300 may also beinstalled in a drive unit of the information recording medium 1, andused as an evaluation unit to perform test recording before a userrecords information on the information recording medium 1.

The signal evaluation index detection section 300 has pattern detectionsections 101, 106 and 111, differential metric computing sections 102,107 and 112, level decision sections 103, 108 and 113, pattern countsections 104, 109 and 114, integration sections 105, 110 and 115, errorcomputing sections 116, 117 and 118, an adding section 119 and astandard deviation computing section 120.

A waveform-shaped digital reproduction signal which is output from thePR equalization section 8, and a binary signal which is output from themaximum likelihood decoding section 9, are input to the signalevaluation index detection section 300. The pattern detection sections101, 106 and 111 compare the transition data sequences in Tables 1, 2and 3 and the binary data which is output from the maximum likelihooddecoding section 9 respectively. If the binary data matches thetransition data sequences in Tables 1, 2 and 3 as a result ofcomparison, the pattern detection sections 101, 106 and 111 selectrespectively a most likely first state transition sequence and a secondmost likely second state transition sequence based on Table 1, Table 2and Table 3.

And based on the selection results of the pattern detection sections101, 106 and 111, the differential metric computing sections 102, 107and 112 compute a metric, which is a distance between an ideal value ofa state transition sequence (PR equalization ideal value: see Table 1,Table 2 and Table 3) and the digital reproduction signal. Then thedifferential metric computing sections 102, 107 and 112 compute thedifference between the metrics computed from the two state transitionsequences respectively, and perform the absolute value processing on themetric differences having plus or minus values.

The outputs from the differential metric computing sections 102, 107 and112 are input to the level decision sections 103, 108 and 113respectively. The level decision sections 103, 108 and 113 comparedifferential metrics computed by the differential metric computingsections 102, 107 and 112 with a predetermined value (signal processingthreshold) respectively. The pattern count sections 104, 109 and 114count a number of differential metrics which are not greater than thesignal processing threshold respectively. These count values each becomea pattern generation frequency when an error rate is calculated. Theintegration sections 105, 110 and 115 integrate the differential metricswhich are not greater than the signal processing threshold respectively.The mean value of the differential metrics which are not greater thanthe signal processing threshold can be determined by dividing theintegration value determined by the integration sections 105, 110 or 115by a number of generated patterns.

Each integration section integrates differential metrics which are notgreater than the signal processing threshold, and each computing sectiondivides each integration value by a number of generated patterns, so asto determine a mean value of the differential metrics which are notgreater than the signal processing threshold, but each integrationsection may integrate the differential metrics which are less than thesignal processing threshold, and each computing section divides eachintegration value by a number of generated patterns, so as to determinea mean value of the differential metrics which are less than the signalprocessing threshold.

The error computing sections 116, 117 and 118 calculate a predictederror rate from each integration value of the differential metrics whichare not greater than the signal processing threshold and the number ofgenerated patterns. The error rates calculated by the error computingsections 116, 117 and 118 are added by the adding section 119. Thestandard deviation corresponding to this error rate is computed by thestandard deviation computing section 120, and this becomes the signalindex value to evaluate the signal quality. The process by this signalevaluation index detection section 300 will now be described in detail.

The reproduction signal reproduced from the information recording medium1 in the PRML processing is output from the maximum likelihood decodingsection 9 as a binary signal, as mentioned above, and is input to thesignal evaluation index detection section 300. When one of thetransition data sequence patterns in Table 1 is detected from thisbinary signal, the PR equalization ideal values of the first statetransition sequence and the second state transition sequence aredetermined. For example, if (0, 0, 0, 0, X, 1, 1, 0, 0,) is decoded asthe binary signal in Table 1, (S0, S1, S2, S3, S5, S6) is selected asthe most likely first state transition sequence, and (S0, S0, S1, S2,S9, S6) is selected as the second most likely second state transitionsequence. The PR equalization ideal value corresponding to the firststate transition sequence is (1, 3, 5, 6, 5). The PR equalization idealvalue corresponding to the second state transition sequence is (0, 1, 3,4, 4).

Then the differential metric computing section 102 determines a firstmetric (Pb₁₄) which is a square of the Euclidean distance between thereproduction signal sequence (digital reproduction signal) and the PRequalization ideal value corresponding to the first state transitionsequence. In the same way, the differential metric computing section 102determines a second metric (Pa₁₄) which is a square of the Euclideandistance between the reproduction signal sequence and the PRequalization ideal value corresponding to the second state transitionsequence. The differential metric computing section 102 determines anabsolute value of the difference of the first metric (Pb₁₄) and thesecond metric (Pb₁₄), as differential metric D₁₄=|Pa₁₄−Pb₁₄|. Thecomputing of Pb₁₄ is shown in Expression (9), and the computing of Pa₁₄is shown in Expression (10). In these expressions, b_(k) denotes a PRequalization ideal value corresponding to the first state transitionsequence, a_(k) denotes a PR equalization ideal value corresponding to asecond state transition sequence, and x_(k) denotes a reproductionsignal sequence.

$\begin{matrix}{{Pb}_{14} = {\sum\limits_{k = {k - 5}}^{k}\left( {x_{k} - b_{k}} \right)^{2}}} & ({E9}) \\{{Pa}_{14} = {\sum\limits_{k = {k - 5}}^{k}\left( {x_{k} - a_{k}} \right)^{2}}} & ({E10}) \\{D_{14} = {{{P\; a_{14}} - {Pb}_{14}}}} & ({E11})\end{matrix}$

In order to determine a signal evaluation index having a highcorrelation with the error rate, an evaluation method considering allthe patterns which have a high possibility of error generation isrequired in the signal processing based on a PR12221 ML system.

FIG. 8 is a diagram depicting the distribution of differential metricsin the signal processing of the PR 12221 ML system. In FIG. 8, thex-axis indicates a differential metric, and the y-axis indicates afrequency of a predetermined differential metric value. As thedifferential metric (square of Euclidean distance) becomes smaller inthe distribution, the possibility of generating an error is higher inthe signal processing based on the PR12221 ML system. As shown in thegraph of FIG. 8, the differential metrics have a distribution group inthe sections 12 and 14, and differential metrics higher than this are 30or more. In other words, in order to acquire a signal index having ahigh correlation with the error rate, it is sufficient to target thedifferential metrics in the groups 12 and 14. These groups indicate thestate transition sequence patterns shown in Table 1, Table 2 and Table3. And the pattern detection sections 101, 106 and 111 identify thesestate transition sequence patterns. This operation of the differentialmetric computing unit, which computes the metric difference from theseidentified state transition sequence patterns, will be described in moredetail.

The distribution of (A) in FIG. 10 is an output frequency distributionof the differential metric computing section 102, the distribution in(B) of FIG. 10 is an output frequency distribution of the differentialmetric computing section 107, and the distribution in (C) of FIG. 10 isan output frequency distribution of the differential metric computingsection 112. FIG. 10(A) shows an output frequency distribution of thedifferential metric computing section 102. The processing of thedifferential metric computing section 107 is shown in Expression (12) toExpression (14), and the processing of the differential metric computingsection 112 is shown in Expression (15) to Expression (17).

$\begin{matrix}{{Pb}_{12A} = {\sum\limits_{k = {k - 7}}^{k}\left( {x_{k} - b_{k}} \right)^{2}}} & ({E12}) \\{{Pa}_{12A} = {\sum\limits_{k = {k - 7}}^{k}\left( {x_{k} - a_{k}} \right)^{2}}} & ({E13}) \\{D_{12A} = {{{Pa}_{12A} - {Pb}_{12A}}}} & ({E14}) \\{{Pb}_{12B} = {\sum\limits_{k = {k - 9}}^{k}\left( {x_{k} - b_{k}} \right)^{2}}} & ({E15}) \\{{Pa}_{12B} = {\sum\limits_{k = {k - 9}}^{k}\left( {x_{k} - a_{k}} \right)^{2}}} & ({E16}) \\{D_{12B} = {{{Pa}_{12B} - {Pb}_{12B}}}} & ({E17})\end{matrix}$

The distributions of (A), (B) and (C) in FIG. 10 have a differentfrequency and center position respectively. A number of error bits whichare generated when each of these patterns cause an error is alsodifferent. The state transition patterns in Table 1, where a square ofthe Euclidean distance is 14, are patterns in which a 1-bit erroroccurs. The state transition patterns in Table 2, where a square of theEuclidean distance is 12, are patterns in which a 2-bit error occurs,and the state transition patterns in Table 3, where a square of theEuclidean distance is 12, are patterns in which a 3-bit error occurs.The error patterns of which the square of the Euclidean distance is 12,in particular, depends on the number of 2Ts that continue, and in thecase of the recording modulation codes which allow a continuation of 6units of 2T, a maximum 6-bit error is generated. Table 3 does not covera 6-bit error in which 2T continuously errors, but a pattern to evaluate2T continuous errors can be defined according to necessity, so as toextrapolate the evaluation target pattern table.

In the state transition sequence patterns in each table, the errorgeneration probability in the recording modulation code sequence is alsodifferent. For example, the error generation frequency is: the statetransition sequence patterns in Table 1 are about 40% of all thesamples, the state transition sequence patterns in Table 2 are about 15%of all the samples, and the state transition sequence patterns in Table3 are about 5% of all the samples. In this way, the distributions shownby (A), (B) and (C) in FIG. 10 have different weights in terms of thestandard deviation a which indicates a dispersion, detection window(Euclidean distance), error generation frequency and error bit count, soprediction of the error rate generated by these distributions can alsobe computed considering these factors. A predicted error ratecalculation method, which is a major characteristic of the presentapplication, will be described below.

As described in the above mentioned problem, when a predicted error rateis calculated for each pattern group, the predicted error rate may notbe able to be determined appropriately depending on the profile ofdistribution. Therefore in the present embodiment, the standarddeviation a is calculated from the mean value of a portion of thedistribution not greater than a predetermined threshold (signalprocessing threshold), so as to determine the error rate, wherebycalculation accuracy of the predicted error rate is improved.

In FIG. 11, the area above the signal processing threshold is an areawhere an error does not occur, and it is therefore unnecessary topredict an error rate. Therefore in order to predict an error rate fromthe standard deviation of the differential metrics, an area not greaterthan the signal processing threshold should be targeted. This error ratecalculation method will now be described. D₁₄, D_(12A) and D_(12B),which are outputs from the differential metric computing sections 102,107 and 112, are input to the level decision sections 103, 108 and 113,and are compared with a predetermined value (signal processingthreshold) respectively. In the present embodiment, the signalprocessing threshold is set to 14 for D₁₄, and 12 for both D_(12A) andD_(12B).

If the differential metric is not greater than the signal processingthreshold, the level decision sections 103, 108 and 113 output thevalue, and increment the count value of the pattern count sections 104,109 and 114 corresponding to the respective pattern count. At the sametime, the integration sections 105, 110 and 115 integrate thedifferential metric that is not greater than the signal processingthreshold. And the error computing sections 116, 117 and 118 calculatethe estimated error rate from the integration value of the differentialmetrics that are not greater than this signal processing threshold andthe number of generated patterns. Operation of these error computingsections 116, 117 and 118 will now be described.

The integration value determined in each integration section 105, 110and 115 is divided by the number of differential metrics (number ofgenerated patterns) that are not greater than the signal processingthreshold, counted by the pattern count section 104, 109 and 114, then amean value of the differential metrics that is not greater than thesignal processing threshold is determined. If it is assumed that themean value of the differential metrics that are not greater than thesignal processing threshold is M(x), a mean value of the distributionfunctions is the standard deviation is σ_(n), the probability densityfunction is f, and the distribution functions have a normaldistribution, then the absolute mean value m of the differential metricsthat are not greater than the signal processing threshold is given bythe following Expression (18).

$\begin{matrix}\begin{matrix}{m = {{M(X)}}} \\{= {\int_{- \infty}^{\infty}{{{x - \mu}}{f(x)}{x}}}} \\{= {\frac{1}{\sqrt{2\pi}\sigma_{n}}\begin{Bmatrix}{{- {\int_{- \infty}^{\mu}{\left( {x - \mu} \right)^{- \frac{{({x - \mu})}^{2}}{2\sigma_{n}^{2}}}{x}}}} +} \\{\int_{\mu}^{\infty}{\left( {x - \mu} \right)^{- \frac{{({x - \mu})}^{2}}{2\sigma_{n}^{2}}}{x}}}\end{Bmatrix}}} \\{= {\frac{\sigma_{n}}{\sqrt{2\; \pi}}\left\{ {{- {\int_{- \infty}^{0}{{te}^{- \frac{t^{2}}{2}}{t}}}} + {\int_{0}^{\infty}{{te}^{- \frac{t^{2}}{2}}{t}}}} \right\}}} \\{= {\frac{\sigma_{n}}{\sqrt{2\pi}}\left( {{- \left\lbrack {- ^{- \frac{t^{2}}{2}}} \right\rbrack_{- \infty}^{0}} + \left\lbrack {- ^{- \frac{t^{2}}{2}}} \right\rbrack_{0}^{\infty}} \right)}} \\{= {\frac{\sigma_{n}}{\sqrt{2\pi}}\left( {{- \left( {- 1} \right)} + 1} \right)}} \\{= {\sqrt{\frac{2}{\pi}}\sigma_{n}}}\end{matrix} & ({E18})\end{matrix}$

Therefore the relationship of the standard deviation σ_(n) of thedifferential metrics that are not greater than the signal processingthreshold and the absolute mean value m of the differential metrics thatare not greater than the signal processing threshold is given by thefollowing Expression (19).

$\begin{matrix}{{m = {{\sqrt{\frac{2}{\pi}}\sigma_{n}} = {0.79788\sigma_{n}}}}{\sigma_{n} = {{\sqrt{\frac{\pi}{2}}m} = {1.25331m}}}} & ({E19})\end{matrix}$

Hence it is understood from Expression (18) and Expression (19) that inorder to calculate the standard deviation σ_(n) of the differentialmetrics that are not greater than the signal processing threshold, theabsolute mean value m of the differential metrics that are not greaterthan the signal processing threshold is determined, and is thenmultiplied by about 1.253. Since the signal processing threshold isfixed, the standard deviation σ_(n) can be calculated from the absolutemean value m. Then the probability of error generation (error rate:bER), calculated by the error computing sections 116, 117 and 118respectively, can be determined by the following Expression (20).

$\begin{matrix}{{bER} = {p \times {\int_{- \infty}^{0}{\frac{1}{\sqrt{2\pi}\sigma_{n}}^{- \frac{{({x - d^{2}})}^{2}}{2\sigma_{n}^{2}}}{x}}}}} & ({E20})\end{matrix}$

Here d in Expression (20) denotes a Euclidean distance between an idealsignal of a most likely first state transition sequence in theextraction target state transition patterns and an ideal signal of asecond most likely second state transition sequence. In the case of thepresent embodiment, a square of the Euclidean distance is d₁₄ ²=14,d_(12A) ²=12 and d_(12B) ²=12.

Therefore if the standard deviations that are determined from theintegration values and integration count by Expression (19) are σ₁₄,σ_(12A) and σ_(12B), then the predicted error rates bER₁₄, bER_(12A) andbER_(12B), which are computed by the error computing sections 116, 117and 118 respectively, are given by the following expressions.

$\begin{matrix}{{bER}_{14} = {1 \times p_{14} \times {\int_{- \infty}^{0}{\frac{1}{\sqrt{2\pi}\sigma_{14}}^{- \frac{{({x - d_{14}^{2}})}^{2}}{2\sigma_{14}^{2}}}{x}}}}} & ({E21}) \\{{bER}_{12A} = {2 \times p_{12A} \times {\int_{- \infty}^{0}{\frac{1}{\sqrt{2\pi}\sigma_{12A}}^{- \frac{{({x - d_{12A}^{2}})}^{2}}{2\sigma_{12A}^{2}}}{x}}}}} & ({E22}) \\{{bER}_{12B} = {3 \times p_{12B} \times {\int_{- \infty}^{0}{\frac{1}{\sqrt{2\pi}\sigma_{12B}}^{- \frac{{({x - d_{12B}^{2}})}^{2}}{2\sigma_{12B}^{2}}}{x}}}}} & \left( {E\; 23} \right)\end{matrix}$

Here P₁₄, P_(12A) and P_(12B) (=0.4, 0.15, 0.05) are error generationprobabilities in the distribution components of all the channel points.An error generated in the state transition sequence patterns in Table 1is a 1-bit error, so the error generation probability has beenmultiplied by 1, an error generated in the state transition sequencepatterns in Table 2 is a 2-bit error, so the error generationprobability has been multiplied by 2, and an error generated in thestate transition sequence patterns in Table 3 is a 3-bit error, so theerror generation probability has been multiplied by 3 respectively. Byadding these error rates, the error generation probability of errorsgenerated in all of the state transition sequence patterns in Table 1,state transition sequence patterns in Table 2, and state transitionsequence patterns in Table 3 can be determined. If the error generationprobability is bER_(all), bER_(all) can be given by the followingExpression (24).

bER_(all) =bER₁₄ +bER_(12A) +bER_(12B)  (E 24)

The standard deviation computing section 120 converts the bit error ratedetermined by Expression (24) into a signal index value, to make it toan index which can be handled in a similar manner as jitter.

$\begin{matrix}{{bER}_{all} = {\frac{p}{2}{{erfc}\left( \frac{1}{2\sqrt{2}M} \right)}}} & ({E25})\end{matrix}$

Here P is a total of P₁₄, P_(12A) and P_(12B), and erfc( ) is anintegration value of a complementary error function. If the definingexpression of the signal index M according to the present invention isExpression (26), then the index value M can be determined bysubstituting bER_(all), calculated by Expression (24), in Expression(25).

$\begin{matrix}{M = \frac{\sigma}{2 \cdot d^{2}}} & ({E26})\end{matrix}$

In the above description, a virtual standard deviation a is calculated,and the signal index value M is calculated from a predicted error rateusing Expressions (20) to (26). However, the calculation method for theevaluation index M according to the present embodiment is not limited tothis method, but may be determined by other defining expressions. Anexample of other defining expressions will now be described.

A probability of pattern Pa to be detected as pattern Pb is assumed tobe the error function given by the following Expression (27).

$\begin{matrix}{{erf}_{t} = {\int_{- \infty}^{0}{\frac{\exp \left\{ {{{- \left( {x - d^{2}} \right)^{2}}/2}\sigma_{t}^{2}} \right\}}{\sqrt{2\pi}\sigma_{t}}{x}}}} & ({E27})\end{matrix}$

Here t in Expression (27) denotes a pattern number of Tables 1 to 3. ddenotes a Euclidean distance in each pattern group in Tables 1 to 3.Specifically, in the case of a pattern group in Table 1, d² is 14, andin the case of the pattern groups in Table 2 and Table 3, d² is 12.

The error generation probability in the pattern group in Table 1, thepattern group in Table 2, and the pattern group in Table 3 can becalculated by the following Expression (28) using Expression (27).

$\begin{matrix}{{bER}_{all} = {{1 \cdot \frac{N_{1}}{N_{1} + N_{2} + N_{3}} \cdot {erf}_{1}} + {2 \cdot \frac{N_{2}}{N_{1} + N_{2} + N_{3}} \cdot {erf}_{2}} + {3 \cdot \frac{N_{3}}{N_{1} + N_{2} + N_{3}} \cdot {erf}_{3}}}} & ({E28})\end{matrix}$

N₁, N₂ and N₃ in Expression (28) are the generation counts of thepattern group defined in Table 1, Table 2 and Table 3 respectively. Thedifference from Expression (24) is that the error rate of each patterngroup is not calculated based on all channels as a parameter, but isbased on the number of evaluation patterns in Table 1 to Table 3 as aparameter. Expression (24) calculates an error rate of which parameteris all the channels including the evaluation patterns. Expression (28),on the other hand, calculates the error rate of which parameter is theevaluation patterns. When a virtual a is calculated based on the errorrates calculated by Expression (24) and Expression (28), a same valuecan be calculated by considering which parameter is the target of a.Expressions (20) to (26) are examples of computations of whichparameters are all channels. The virtual a is calculated based onExpression (28), and the evaluation index M is calculated.

The virtual standard deviation a can be calculated by the followingExpression (29).

σ=E ⁻¹(bER_(all))  (E 29)

Here E⁻¹ denotes an inverse function of Expression (30).

$\begin{matrix}{{E(\sigma)} = \left\lbrack {\int_{- \infty}^{- 1}{\frac{1}{\sqrt{2\; \pi}\sigma} \cdot^{- \frac{x^{2}}{2\sigma^{2}}}{x}}} \right\rbrack} & ({E30})\end{matrix}$

The evaluation index M can be calculated using the following Expression(31), by normalizing with a detected window.

$\begin{matrix}{M = \frac{\sigma}{2}} & ({E31})\end{matrix}$

In the end, Expression (26) and Expression (31) calculate a virtual awhich is generated in the evaluation patterns defined in Table 1 toTable 3, so the index value M is calculated as substantially a samevalue. The only difference is the evaluation parameter to calculate theerror rate and the notation of the detection window. Either expressionmay be used to calculate the signal index value M. The calculation ofthe signal index value M using Expression (31) can also be applied tothe first embodiment, of which extraction target is only specific statetransition patterns.

FIG. 12 is an example of a simulation result showing the bit error rate(bER) and the signal index value % of Expression (18) when reproductionstress, such as tile, defocus and spherical aberration, is applied. Inthe graph in FIG. 12, ▴ (black triangle) indicates a defocus stress, (black circle) indicates a spherical aberration stress, ♦ (blackdiamond) indicates a radical tilt stress, and ▪ (black square) indicatesa tangential tilt stress. The solid line in FIG. 12 is a theoreticalcurve.

Generally the criteria of the system margin is a bER of about 4.0 E-4,so the signal index value to implement this bER is about 15%. As shownin the graph of FIG. 12, the signal index value M, defined in thepresent embodiment, is matched with the theoretical curve of the errorrate in the area of the signal index value ≦15%, which is actually usedin the system. Therefore the signal evaluation method and indexaccording to the present embodiment are very effective in terms ofevaluating signals appropriately.

As described above, according to the present embodiment, statetransition sequence patterns of merging paths, of which Euclideandistance in the PRML signal processing is relatively short, aretargeted, and one signal evaluation index is generated based on thedifferential metric information of a plurality of pattern groups, havinga different error generation probability and a different number oferrors to be generated. Specifically, predicted error rates arecalculated from the mean values of the differential metric information,which are not greater than the signal processing threshold of eachpattern, the total thereof is calculated, then a virtual standarddeviation (hereafter σ) of normal distribution is calculated from thetotal of error rates, and the signal evaluation index, including thisstandard deviation a of the normal distribution, is generated. As aresult, it is possible to realize a signal evaluation method andevaluation index having very high correlation with the error rate.

The pre-amplifier section 3, the AGC section 4 and the waveformequalization section 5 in the present embodiment in FIG. 2 may beconstructed as one analog integrated circuit (LSI). The pre-amplifiersection 3, the AGC section 4, the waveform equalization section 5, theA/D conversion section 6, the PLL section 7, the PR equalization section8, the maximum likelihood decoding section 9, the signal evaluationindex detection section 100, and the optical disk controller section 15may be consolidated as one analog-digital-mixed integrated circuit(LSI).

In each of the above mentioned embodiments, a case of using thereproduction device as the optical disk device was described. However,needless to say, the optical disk device of the present invention is notlimited to this, but can also be applied to a recording/reproductiondevice. In this case, circuits for recording are added, but descriptionthereof is omitted here, since a known circuit structure can be used.

Third Embodiment

An optical disk device according to still another embodiment of thepresent invention will now be described with reference to the drawings.

FIG. 13 is a block diagram depicting a general structure of the opticaldisk device of the present embodiment.

The optical disk device 600 has: an optical head section 2, apre-amplifier section 3, an AGC (Automatic Gain Controller) section 4, awaveform equalization section 5, an A/D conversion section 6, a PLL(Phase Locked Loop) section 7, a PR equalization section 8, a maximumlikelihood decoding section 9, a signal evaluation index detectionsection (reproduction signal evaluation unit) 500 and an optical diskcontroller section 15. The structures and functions of these composingelements constituting the optical disk device 600 are the same as thefirst embodiment, and descriptions thereof are omitted here.

The optical disk device 600 according to the present embodiment has asignal evaluation index detection section 500 as the reproduction signalevaluation unit. The signal evaluation index detection section 500 hasthe same structure as the signal evaluation index detection section 100of the first embodiment, except for the setting of the signal processingthreshold. Hence composing elements having a similar structure andfunction as the signal evaluation index detection section 100 of thefirst embodiment are denoted with a same symbol, and description thereofis omitted.

As shown in FIG. 13, the signal evaluation index detection section 500has a mean value computing section 121 for computing a mean value ofoutputs of the differential metric computing section 102, in addition tothe structure of the first embodiment.

Now the operation of the mean value computing section 121 and how to setthe signal processing threshold will be described. In the firstembodiment, a predetermined value, that is, a code distance of idealsignals (a square of Euclidean distance between an ideal signal of amost likely first state transition sequence and an ideal signal of asecond most likely second state transition sequence in a specificextraction target state transition pattern) is used as the signalprocessing threshold. This is because in optimized recording, the meanvalue of outputs of the differential metric computing section matchesthe code distance of the ideal signals. However, as recording densitiesof optical disks further improve, recording optimization, to match themean value with the code distance of the ideal signals, may not bepossible in some cases.

Therefore the signal evaluation index detection section 500 of thepresent embodiment has the mean value computing section 121 forcomputing a mean value of outputs of the differential metric computingsection 102, and inputs this mean value to the level decision section103 as the signal processing threshold.

According to the foregoing structure, the signal processing thresholdcan be appropriately set at the center of distribution, which is outputfrom the differential metric computing section 102. Thereby correlationof the signal index value and the bit error rate, when the recordingdensity is increased, can be improved compared with the structure of thefirst embodiment.

Therefore the structure of the present embodiment, using the mean valueof the differential metric distribution as the signal processingthreshold, is particularly useful when a high density recording mediumis adopted as the information recording medium 1.

Fourth Embodiment

An optical disk device according to still another embodiment of thepresent invention will now be described with reference to the drawings.

FIG. 14 is a block diagram depicting the general structure of theoptical disk device of the present invention.

The optical disk device 800 has an optical head section 2, apreamplifier section 3, an AGC (Automatic Gain Controller) section 4, awaveform equalization section 5, an A/D conversion section 6, PLL (PhaseLocked Loop) section 7, a PR equalization section 8, a maximumlikelihood decoding section 9, a signal evaluation index detectionsection (reproduction signal evaluation unit) 700 and an optical diskcontroller section 15. The structure and function of these composingelements constituting the optical disk device 800 are the same as thesecond embodiment, so description thereof is omitted here.

The optical disk device 800 according to the present embodiment has asignal evaluation index detection section 700 as the reproduction signalevaluation unit 700. The signal evaluation index detection section 700has the same structure as the signal evaluation index detection section300 of the second embodiment, except for the setting of the signalprocessing threshold. Hence a composing element having a similarstructure and function as the signal evaluation index detection section300 of the second embodiment is denoted with a same symbol, anddescription thereof is omitted.

As shown in FIG. 14, the signal evaluation index detection section 700has mean value computing sections 121, 122 and 123 for computing arespective mean value of the outputs of the differential metriccomputing sections 102, 107 and 112, in addition to the structure of thesecond embodiment.

Now operation of the mean value computing sections 121, 122 and 123, andhow to set the signal processing threshold will be described. In thethird embodiment, a predetermined value, that is, a code distance ofideal signals (a square of Euclidean distance between an ideal signal ofa most likely first state transition sequence and an ideal signal of asecond most likely second state transition sequence in each extractiontarget state transition pattern), is used as the signal processingthreshold. This is because in optimized recording, the mean value ofoutputs of the differential metric computing section matches the codedistance of ideal signals. However, as the recording densities ofoptical disks further improve, recording optimization to match the meanvalue with the code distance of the ideal signals may not be possible insome cases.

Therefore the signal evaluation index detection section 700 of thepresent embodiment has the mean value computing sections 121, 122 and123 for computing a respective mean value of outputs of the differentialmetric computing sections 102, 107 and 112, and inputs this mean valueto the level decision sections 103, 108 and 113 as the signal processingthreshold respectively.

According to the foregoing structure, the signal processing thresholdcan be appropriately set at the center of distribution, which is outputfrom each of the differential metric computing sections 102, 107 and112. Thereby correlation of the signal index value and the bit errorrate when the recording density is increased can be improved comparedwith the structure of the first embodiment.

Therefore the structure of the present embodiment, using the mean valueof the differential metric distribution as the signal processingthreshold, is particularly useful when a high density recording mediumis adopted as the information recording medium 1.

Fifth Embodiment

An optical disk device according to the fifth embodiment will now bedescribed. FIG. 15 is a block diagram depicting the general structure ofthe optical disk device of the fifth embodiment of the presentinvention.

The optical disk device 920 has an optical head section 2, apreamplifier section 3, an AGC section 4, a waveform equalizationsection 5, an A/D conversion section 6, a PLL section 7, a PRequalization section 8, a maximum likelihood decoding section 9, asignal evaluation index detection section (reproduction signalevaluation unit) 910, and an optical disk controller section 15. Thestructure and function of part of these composing elements constitutingthe optical disk device 920 are the same as the first to fourthembodiments, so descriptions thereof are omitted here.

The optical disk device 920 according to the fifth embodiment has asignal evaluation index detection section 910 as the reproduction signalevaluation unit. The signal evaluation index detection section 910 ofthe fifth embodiment has the same structure as the signal evaluationindex detection section of the first and third embodiments, other thanthe computing processing for determining the standard deviation of thedifferential metrics. Hence a composing element, having a similarstructure and function as the signal evaluation index detection section100 of the first embodiment is denoted with a same symbol anddescriptions thereof, are omitted.

Now a structure of the signal evaluation index detection section 910according to the fifth embodiment will be described. The signalevaluation index detection section 910, just like the signal evaluationindex detection section of the first to fourth embodiments, can be usedas a reproduction signal evaluation unit for judging whether the qualityof the information recording medium 1 conforms to a predeterminedstandard before shipment. The present signal evaluation index detectionsection 910 may also be installed in a drive unit of the informationrecording medium 1, and be used as an evaluation unit to perform testrecording before a user records information on the information recordingmedium 1.

The signal evaluation index detection section 910 has a patterndetection section 101, a differential metric computing section 102, alevel decision section 103, a pattern count section 104, an integrationsection 105, an error computing section 116, a pattern count section124, an integration section 125 and a standard deviation computingsection 120.

As FIG. 15 shows, the signal evaluation index detection section 910 hasthe integration section 125 for computing the mean value of the outputsof the differential metric computing section 102 and the pattern countsection 124 for counting the outputs of the differential metriccomputing section 102, in addition to the configuration of the firstembodiment.

The signal evaluation index detection section 910 evaluates the qualityof a reproduction signal reproduced from an information recording mediumbased on a binary signal generated from the reproduction signal using aPRML signal processing system. The pattern detection section 101extracts, from the binary signal, a specific state transition patternwhich has the possibility of causing a bit error.

The differential metric computing section 102 computes a differentialmetric, which is a difference of a first metric between an ideal signalof a most likely first state transition sequence corresponding to thebinary signal and the reproduction signal, and a second metric betweenan ideal signal of a second most likely second state transition sequencecorresponding to the binary signal and the reproduction signal, based onthe binary signal of the state transition pattern extracted by thepattern extraction section.

The integration section 125 integrates the differential metric computedby the differential metric computing section 102. The pattern countsection 124 counts a number of times of integration processing by theintegration section 125 by counting a number of times of patterngeneration in the pattern detection section 101.

The level decision section 103 extracts the differential metric which isnot greater than a predetermined signal processing threshold. Theintegration section 105 integrates the differential metric which is notgreater than the signal processing threshold extracted by the leveldecision section 103. The pattern count section 104 counts a number oftimes of integration processing by the integration section 105.

The error computing section 116 computes an error rate that is predictedbased on the integration value that is integrated by the integrationsection 125, the count value that is counted by the pattern countsection 124, the integration value that is integrated by the integrationsection 105, and the count value that is counted by the pattern countsection 104.

The error computing section 116 also computes an error rate based on amean value of the differential metrics computed based on the integrationvalue that is integrated by the integration section 125 and the countvalue that is counted by the pattern count section 124, and apredetermined computing result based on the integration value that isintegrated by the integration section 105 and the count value that iscounted by the pattern count section 104.

The error rate computing section 116 also computes a standard deviationof a differential metric of which differential metric output is the meanvalue or less, using a linear expression of which arguments are theintegration value that is integrated by the integration section 125, thecount value that is counted by the pattern count section 124, theintegration value that is integrated by the integration section 105 andthe count value that is counted by the pattern count section 104, andcomputes the error rate based on the standard deviation. The linearexpression is an approximate expression that is computed using iterationbased on the Newton's method.

The standard deviation computing section 120 computes a standarddeviation based on the error rate that is computed by the error ratecomputing section 116.

The pattern count section 124 counts the number of times of generationof a specific pattern that is detected by the pattern detection section101 and outputs the count value N₁. The integration section 125integrates the output from the differential metric computing section 102and outputs the integration value S₁. The integration section 105integrates the output result by the level decision section 103 andoutputs the integration value JS₁. The pattern count section 104 countsa number of time when the conditions are met in the level decisionsection 103, and outputs the count value JN₁. The structures of thecomposing elements, other than the integration section 125 and thepattern count section 124 for computing the mean value of thedifferential metrics of each pattern group, are the same as the firstembodiment, and the descriptions thereof are omitted here.

In the fifth embodiment, the signal evaluation index detection section910 corresponds to an example of the reproduction signal evaluationunit, the pattern detection section 101 corresponds to an example of thepattern extraction section, the differential metric computing section102 corresponds to an example of the differential metric computingsection, the integration section 125 corresponds to an example of thefirst integration section, the pattern count section 124 corresponds toan example of the first count section, the level decision section 103corresponds to an example of the differential metric extraction section,the integration section 105 corresponds to an example of the secondintegration section, the pattern count section 104 corresponds to anexample of the second count section, the error computing section 116corresponds to an example of the error rate computing section, and thestandard deviation computing section 120 corresponds to an example ofthe standard deviation computing section.

Now the computation of the standard deviation of the differential metricfor computing the predicted error rate according to the fifth embodimentwill be described. The structure proposed by the third and fourthembodiments is where a mean value of the differential metrics isdetermined when the mean value of the outputs from the differentialmetric computing section does not match with the code distance of theideal signal depending on the recording state (quality), and a predictederror rate is computed from the standard deviation of the differentialmetric that is determined based on this mean value, so as to improve thecorrelation of an actually generated error rate and the signal indexvalue.

With this configuration of the third and fourth embodiments, however,two problems can occur. One, since the mean value of the differentialmetrics in the signal index value measurement area is determined, themean value must be determined in advance. In order to compute the signalindex value, a plurality of times of measurements are required, and thisprocessing may take time. Two, is that a possible solution for the firstproblem is to update the mean value while computing the mean value.However the optimum value of the response characteristic of computingthe mean value could differ in some cases depending on the recordingstate. Therefore it is difficult to unequivocally determine the responsecharacteristic of computing the mean value for obtaining compatiblesignal index values.

In order to solve these problems, the fifth embodiment suggests acalculation method that uses a predetermined fixed value called the“code distance of an ideal signal” as the signal processing threshold inthe processing for determining a standard deviation from the output ofthe differential metric, just like the first embodiment. Then if themean value of the outputs of the differential metric computing section102 does not match with the code distance of the ideal signal dependingon the recording state (quality), an error of the standard deviation,that is generated by a shift in the mean value, is corrected so that theinsufficient correlation between the signal index value and the biterror rate is solved.

FIG. 16A and FIG. 16B are diagrams depicting a distributions in thedifferential metric ranges in a specific recording state. Thedistributions in FIG. 16A and FIG. 16B are examples when the mean valueof the outputs of the differential metrics does not match with the codedistance of the ideal signal. FIG. 16A is a diagram depictingdistribution in the differential metric range used for determining thestandard deviation in the third and fourth embodiments. The third andfourth embodiments determine the mean value of the distribution, thendetermine the standard deviation from the differential metric values ina range smaller than the mean value, and compute the predicted errorrate, whereby the reproduction signal evaluation method that does notdepend on the recording quality can be provided. The fifth embodiment,on the other hand, determines the standard deviation using a fixedsignal processing threshold, so as to obtain an effect similar to thethird and fourth embodiments.

FIG. 16B is a diagram depicting a distribution in the differentialmetric range that is used for determining the standard deviation in thefifth embodiment. According to the fifth embodiment, a predeterminedcorrection is performed on the differential metric values in a rangesmaller than a fixed signal processing threshold, whereby the standarddeviation corresponding to the third and fourth embodiments can bedetermined.

Now a method for calculating the standard deviation according to thefifth embodiment will be described. First the parameters to be used forthe calculation in the fifth embodiment are redefined. S₁ is anintegration value of the differential metrics, N₁ is a frequency of thedifferential metrics (count value to indicate a number of times ofintegration of S₁), JS₁ is an integration value of the differentialmetrics which are not greater than the signal processing threshold (“0”in this case), JN₁ is a frequency of differential metrics which are notgreater than the signal processing threshold (“0” in this case) (countvalue to indicate a number of times of integration of JS₁), σ is apredetermined frequency coefficient, and E₁ is an ideal signalprocessing value.

FIG. 17A and FIG. 17B are diagrams depicting the calculation method forthe standard deviation according to the fifth embodiment. In order todetermine a virtual standard deviation σ₁′ from the integration valueJS₁ indicated by the hatched portion shown in FIG. 17A, the integrationvalue JS₁ must be normalized by the count value N₁. According to thefifth embodiment, a virtual standard deviation is determined for thepattern group in Table 1. E1 indicates a detection window, and 14 isinserted for the pattern group in Table 1.

The count value N₁ can be determined by the following Expression (32).

$\begin{matrix}{N_{1} = {\frac{\alpha}{\sqrt{2\pi}\sigma_{1}^{\prime}}{\int_{- E_{1}}^{E_{1}}{^{\frac{- {({x - \mu})}^{2}}{2\sigma_{1}^{\prime 2}}}{x}}}}} & ({E32})\end{matrix}$

The count value JN₁ can be determined by the following Expression (33).

$\begin{matrix}{{JN}_{1} = {\frac{\alpha}{\sqrt{2\pi}\sigma_{1}^{\prime}}{\int_{- E_{1}}^{0}{^{\frac{- {({x - \mu})}^{2}}{2\sigma_{1}^{\prime \; 2}}}{x}}}}} & ({E33})\end{matrix}$

The mean value μ of the distributions of Expression (32) and Expression(33) is defined by the following Expression (34).

$\begin{matrix}{\mu = \frac{S_{1}}{N_{1}}} & ({E34})\end{matrix}$

The calculation for normalizing the integration value JS₁ by the countvalue N₁ is given by the following Expression (35).

$\begin{matrix}\begin{matrix}{\frac{{JS}_{1}}{N_{1}} = \frac{\frac{\alpha}{\sqrt{2\pi}\sigma_{1}^{\prime}}{\int_{- E_{1}}^{0}{{- x} \times ^{\frac{- {({x - \mu})}^{2}}{2\sigma_{1}^{\prime \; 2}}}{x}}}}{\frac{\alpha}{\sqrt{2\; \pi}\sigma_{1}^{\prime}}{\int_{- E_{1}}^{E_{1}}{^{\frac{- {({x - \mu})}^{2}}{2\sigma_{1}^{\prime \; 2}}}{x}}}}} \\{= \frac{\frac{\alpha}{\sqrt{2\pi}\sigma_{1}^{\prime}}{\int_{{- E_{1}} - \mu}^{- \mu}{{- \left( {x^{\prime} + \mu} \right)} \times ^{\frac{- x^{\prime \; 2}}{2\sigma_{1}^{\prime \; 2}}}{x^{\prime}}}}}{\frac{\alpha}{\sqrt{2\pi}\sigma_{1}^{\prime}}{\int_{{- E_{1}} - \mu}^{E_{1} - \mu}{^{\frac{- x^{\prime \; 2}}{2\sigma_{1}^{\prime \; 2}}}{x^{\prime}}}}}} \\{\cong {{\frac{\sigma_{1}^{\prime}}{\sqrt{2\pi}}^{- \frac{\mu^{2}}{2\sigma_{l}^{\prime \; 2}}}} - \frac{S_{1}{JN}_{1}}{N_{1}^{2}}}}\end{matrix} & ({E35})\end{matrix}$

In other words, the above Expression (35) can be transformed into thefollowing Expression (36).

$\begin{matrix}{{\left( {\frac{{JS}_{1}}{N_{1}E_{1}} - \frac{S_{1}{JN}_{1}}{E_{1}N_{1}^{2}}} \right)\sqrt{2\pi}} = {\left( \frac{\sigma_{1}^{\prime}}{E_{1}} \right)^{- \frac{{(\frac{S_{1}}{N_{I}E_{I}})}^{2}}{2{(\frac{\sigma_{1}^{\prime}}{E_{1}})}^{2}}}}} & ({E36})\end{matrix}$

Using two variables, which are a₁ given by the following Expression (37)and b₁ given by the following Expression (38), the Expression (36) canbe simplified into the following Expression (39).

$\begin{matrix}{{\left( {\frac{{JS}_{1}}{N_{1}E_{1}} - \frac{S_{1}{JN}_{1}}{E_{1}N_{1}^{2}}} \right)\sqrt{2\pi}} = a_{1}} & ({E37}) \\{\frac{S_{1}}{N_{1}E_{1}} = b_{1}} & ({E38}) \\{a_{1} = {\left( \frac{\sigma_{1}^{\prime}}{E_{1}} \right)^{- \frac{{(b_{1})}^{2}}{2{(\frac{\sigma_{1}^{\prime}}{E_{1}})}^{2}}}}} & ({E39})\end{matrix}$

A conversion for determining the standard deviation σ₁ considering theshift of the mean value of the distribution based on the variable a₁given by Expression (37) and the variable b₁ given by Expression (38)(FIG. 17B) will be described next.

The above Expression (39), which is defined as a function of whicharguments are the standard deviation σ_(l) and variable b₁, can be givenby the following Expression (40).

a ₁ =W(σ₁ ,b ₁)  (E 40)

If the virtual standard deviation σ₁′ shown in FIG. 17A is moved to theleft side of the Expression (40), the following Expression (41) isestablished.

$\begin{matrix}{\frac{\sigma_{1}^{\prime}}{E_{1}} = {{W^{- 1}\left( {a_{1},b_{1}} \right)} \cong {{{P^{\prime}\left( b_{1} \right)}a_{1}} + {Q^{\prime}\left( b_{1} \right)}}}} & ({E41})\end{matrix}$

The index, in which the shift of the mean value of the distributionshown in FIG. 17B is reflected in the detection window, can be definedas the following Expression (42).

$\begin{matrix}\begin{matrix}{\frac{\sigma_{1}}{2} = \frac{\sigma_{1}^{\prime}}{2\left( {1 + \frac{\mu}{E_{1}}} \right)}} \\{= \frac{\sigma_{1}^{\prime}}{2\left( {1 + b_{1}} \right)E_{1}}} \\{= \frac{{{P^{\prime}\left( b_{1} \right)}a_{1}} + {Q^{\prime}\left( b_{1} \right)}}{2\left( {1 + b_{1}} \right)}} \\{= {{{P\left( b_{1} \right)}a_{1}} + {Q\left( b_{1} \right)}}}\end{matrix} & ({E42})\end{matrix}$

σ₁/2 for the two variables a₁ and b₁, that satisfies the aboveExpression (42), is calculated by the Newton's method. The Newton'smethod is one of the algorithms for solving equations based oniteration, which is used for solving equations by numerical calculationin the numerical analysis field, and has been used for numericalcalculation for a long time. Here descriptions on the algorithm of theNewton's method are omitted.

FIG. 18 is a graph depicting a relationship of a variable a₁(a_(x)) andstandard deviation σ₁/2(σ_(x)/2) computed by a Newton's method, which isshown for each shift amount of the mean value of the outputs of thedifferential metrics. In FIG. 18, the horizontal axis indicates variablea₁(a_(x)) [%], which is determined by the above Expression (37), and thevertical axis indicates σ₁/2 (σ_(x)/2) [%] that is calculated by aNewton's method. The shift amount of the mean value of the outputs ofthe differential metrics is a variable b₁ that is determined by theabove Expression (38). The relationship of σ₁/2 [%] and the variable a₁shown in FIG. 18 can be expression by a linear expression of whichvariable is b₁. Hence σ₁/2, which is determined by the Newton's method,can be expression by the linear expression of which variable b₁ is themean value of the outputs of the differential metrics.

In the linear expression of Expression (42), P denotes an inclinationwhen the mean value of the outputs of the differential metrics is thevariable b₁, and Q denotes an intercept when the mean value of theoutputs of the differential metrics is the variable b₁. The value of Pand the value of Q may be stored in a table with respect to b₁determined by approximate calculation. Table 4 shows a concrete exampleof a table which indicates the value P and the value Q of which argumentis the variable b_(x). x in the variable b_(x) in Table 4 contributesdetermining the standard deviation σ_(x) respectively for pattern groupsin Table 1, Table 2 and Table 3, and values “1”, “2” or “3”corresponding to Table 1, Table 2 or Table 3 is inserted respectively.

TABLE 4 b_(x) P(b_(x)) Q(b_(x)) −0.300 0.6470 0.0975 −0.295 0.63740.0958 −0.290 0.6283 0.0940 −0.285 0.6195 0.0923 −0.280 0.6112 0.0905−0.275 0.6034 0.0887 −0.270 0.5959 0.0869 −0.265 0.5887 0.0852 −0.2600.5820 0.0834 −0.255 0.5756 0.0816 −0.250 0.5696 0.0798 −0.245 0.56390.0779 −0.240 0.5586 0.0761 −0.235 0.5535 0.0743 −0.230 0.5488 0.0724−0.225 0.5445 0.0705 −0.220 0.5404 0.0686 −0.215 0.5366 0.0668 −0.2100.5331 0.0648 −0.205 0.5299 0.0629 −0.200 0.5270 0.0610 −0.195 0.52430.0590 −0.190 0.5219 0.0571 −0.185 0.5198 0.0551 −0.180 0.5179 0.0531−0.175 0.5162 0.0511 −0.170 0.5148 0.0491 −0.165 0.5135 0.0471 −0.1600.5125 0.0451 −0.155 0.5117 0.0431 −0.150 0.5110 0.0410 −0.145 0.51050.0390 −0.140 0.5102 0.0370 −0.135 0.5100 0.0350 −0.130 0.5100 0.0330−0.125 0.5100 0.0310 −0.120 0.5102 0.0290 −0.115 0.5105 0.0270 −0.1100.5108 0.0251 −0.105 0.5112 0.0232 −0.100 0.5116 0.0213 −0.095 0.51200.0195 −0.090 0.5125 0.0177 −0.085 0.5129 0.0160 −0.080 0.5133 0.0143−0.075 0.5136 0.0127 −0.070 0.5138 0.0112 −0.065 0.5140 0.0097 −0.0600.5140 0.0084 −0.055 0.5139 0.0071 −0.050 0.5136 0.0059 −0.045 0.51320.0048 −0.040 0.5126 0.0038 −0.035 0.5118 0.0029 −0.030 0.5108 0.0021−0.025 0.5096 0.0015 −0.020 0.5081 0.0010 −0.015 0.5064 0.0005 −0.0100.5045 0.0002 −0.005 0.5024 0.0001 0.000 0.5000 0.0000 0.005 0.49740.0001 0.010 0.4945 0.0002 0.015 0.4915 0.0005 0.020 0.4882 0.0009 0.0250.4847 0.0014 0.030 0.4810 0.0020 0.035 0.4772 0.0027 0.040 0.47320.0035 0.045 0.4690 0.0044 0.050 0.4647 0.0053 0.055 0.4603 0.0063 0.0600.4558 0.0074 0.065 0.4512 0.0085 0.070 0.4466 0.0097 0.075 0.44190.0109 0.080 0.4372 0.0122 0.085 0.4325 0.0135 0.090 0.4278 0.0148 0.0950.4232 0.0161 0.100 0.4186 0.0174 0.105 0.4140 0.0188 0.110 0.40960.0201 0.115 0.4052 0.0215 0.120 0.4009 0.0228 0.125 0.3967 0.0241 0.1300.3926 0.0254 0.135 0.3887 0.0267 0.140 0.3849 0.0279 0.145 0.38120.0291 0.150 0.3777 0.0303 0.155 0.3743 0.0315 0.160 0.3711 0.0326 0.1650.3681 0.0338 0.170 0.3652 0.0348 0.175 0.3624 0.0359 0.180 0.35990.0369 0.185 0.3575 0.0379 0.190 0.3553 0.0388 0.195 0.3532 0.0398 0.2000.3513 0.0407 0.205 0.3496 0.0415 0.210 0.3481 0.0423 0.215 0.34670.0431 0.220 0.3455 0.0439 0.225 0.3445 0.0446 0.230 0.3436 0.0453 0.2350.3429 0.0460 0.240 0.3423 0.0466 0.245 0.3420 0.0473 0.250 0.34170.0479 0.255 0.3417 0.0484 0.260 0.3418 0.0490 0.265 0.3421 0.0495 0.2700.3425 0.0500 0.275 0.3431 0.0504 0.280 0.3438 0.0509 0.285 0.34470.0513 0.290 0.3458 0.0517 0.295 0.3470 0.0521 0.300 0.3484 0.0525

In the example in Table 4, the correction table is unequivocally definedin a −30% to +30% correction range, but the correction range may bewidened or narrowed. For the correction range, it is preferable tosupport the range considering the actually generated shift amount. Theargument b_(x) of P(b_(x)) and Q(b_(x)) in Table 4 is indicated with0.05 spacing as an example. If a value in the space of the variablesb_(x) which are stored in advance (e.g. 0.025) is input here as thevariable b_(x), then P(b_(x)) and Q(b_(x)) corresponding to thevariables b_(x) before and after the input value, out of the variablesb_(x) stored with 0.05 spacing in Table 4, may be linearly interpolatedrespectively. P(b_(x)) and Q(b_(x)) corresponding to the variable b_(x)closest to the input value may also be selected out of the variableb_(x) stored in advance.

In this way, according to the fifth embodiment, correction computing fordetermining the standard deviation σ₁/2, considering the shift of themean value of the distribution, is performed using the integration value(JS₁) and a number of times of integration (JN₁) based on the shiftamount (S₁/N₁) of the distribution of the outputs of the differentialmetric computing section 102 and the fixed signal processing threshold.In the fifth embodiment, a simple linear expression given by theExpression (42) is used for the correction expression to improve thepredicted error rate computing accuracy.

In the fifth embodiment, the predicted error rate is determined usingthe standard deviation σ₁/2 that is determined by the Expression (42)according to one of the patterns in Table 1 to Table 3. Thereby a signalindex value which has a high correlation with the error rate can bedetermined even if the center of the distribution of the outputs of thedifferential metric computing section 102 is shifted from the signalprocessing threshold as in FIG. 21B and FIG. 21C.

Sixth Embodiment

An optical disk device having a reproduction signal evaluation unitaccording to the sixth embodiment of the present invention will now bedescribed with reference to the drawings. A composing element that isthe same as the fifth embodiment is denoted with a same element numberfor which description can be omitted. FIG. 19 is a block diagramdepicting the structure of the optical disk device 940 of the sixthembodiment.

An information recording medium 1 is an information recording medium foroptically recording/reproducing information, and is an optical diskmedium, for example. The optical disk device 940 is a reproduction unitwhich reproduces information from the installed information recordingmedium 1.

The optical disk device 940 has an optical head section 2, apreamplifier section 3, an AGC section 4, a waveform equalizationsection 5, an A/D conversion section 6, a PLL section 7, a PRequalization section 8, a maximum likelihood decoding section 9, asignal evaluation index detection section (reproduction signalevaluation unit) 930, and an optical disk controller section 15. Thestructures and functions of a part of these elements constituting theoptical disk device 940 are the same as the first to fifth embodiments,and descriptions thereof are omitted here.

Now a structure of the signal evaluation index detection section 930according to the sixth embodiment will be described. The signalevaluation index detection section 930, just like the signal evaluationindex detection section of the first to fifth embodiments, can be usedas a reproduction signal evaluation unit for judging whether the qualityof the information recording medium 1 conforms to a predeterminedstandard before shipment. The present signal evaluation index detectionsection 930 may also be installed in a drive unit of the informationrecording medium 1, and used as an evaluation unit to perform testrecording before a user records information on the information recordingmedium 1.

The signal evaluation index detection section 930 has pattern detectionsections 101, 106 and 111, differential metric computing sections 102,107 and 112, level decision sections 103, 108 and 113, pattern countsections 104, 109 and 114, integration section 105, 110 and 115, errorcomputing sections 116, 117 and 118, pattern count sections 124, 126 and128, integration sections 125, 127 and 129, an addition section 119, anda standard deviation computing section 120.

In addition to the structure in the first embodiment, the signalevaluation index detection section 930 has integration sections 125, 127and 129 for computing a mean value of the outputs of the differentialmetric computing sections 102, 107 and 112, and pattern count sections124, 126 and 128 for counting the output of the differential metriccomputing section 102, as shown in FIG. 19.

The pattern detection sections 101, 106 and 111 extract, from the binarysignal, a state transition pattern which has the possibility of causinga bit error respectively. The differential metric computing sections102, 107 and 112 calculate a differential metric, which is a differenceof the first metric between an ideal signal of a most likely first statetransition sequence corresponding to the binary signal and thereproduction signal, and a second metric between an ideal signal of asecond most likely second state transition sequence corresponding to thebinary signal and the reproduction signal based on the binary signal foreach state transition pattern extracted by the pattern detectionsections 101, 106 and 111 respectively.

The integration sections 125, 127 and 129 integrate the differentialmetric computed by the differential metric computing sections 102, 107and 112 for each state transition patterns respectively. The patterncount sections 124, 126 and 128 count a number of times of integrationprocessing by the integration sections 125, 127 and 129 for each statetransition pattern respectively.

The level decision sections 103, 108 and 113 extract differentialmetrics not greater than a predetermined signal processing threshold foreach state transition pattern respectively. The integration sections105, 110 and 115 integrate the differential metrics which are notgreater than the signal processing threshold extracted for each statetransition pattern by the level decision section 103, 108 and 113respectively. The pattern count sections 104, 109 and 114 count thenumber of times of integration processing by the integration sections105, 110 and 115 for each state transition pattern respectively.

The error computing sections 116, 117 and 118 calculate a plurality oferror rates predicted based on the plurality of integration valuesintegrated by the integration sections 125, 127 and 129, the pluralityof count values counted by the pattern count sections 124, 126 and 128,the plurality of integration values integrated by the integrationsections 105, 110 and 115, and the plurality of count values counted bythe pattern count sections 104, 109 and 114 for each state transitionpattern respectively.

The standard deviation computing section 120 computes a standarddeviation based on the total of the plurality of error rates that arecomputed by the error computing sections 116, 117 and 118.

The pattern count sections 124, 126 and 128 count a number of times ofgeneration of a specific pattern detected by the pattern detectionsections 101, 106 and 111, and output the count values N₁, N₂ and N₃respectively. The integration sections 125, 127 and 129 integrate theoutputs of the differential metric computing sections 102, 107 and 112,and output the integration values S₁, S₂ and S₃ respectively. Theintegration sections 105, 110 and 115 integrate the output results ofthe level decision sections 103, 108 and 113, and output the integrationvalues JS₁, JS₂ and JS₃ respectively. The pattern count sections 104,109 and 114 count a number of times the conditions are met in the leveldecision sections 103, 108 and 113, and output the count values JN₁, JN₂and JN₃ respectively. A structure other than the integration sections125, 127 and 129 for computing a mean value of the differential metricsof each pattern group and the pattern count sections 124, 126 and 128are exactly the same as the first embodiment, and detailed descriptionsthereof are therefore omitted.

In the sixth embodiment, the signal evaluation index detection section930 corresponds to the example of the reproduction signal evaluationunit, the pattern detection sections 101, 106 and 111 correspond to anexample of the pattern extraction section, the differential metriccomputing sections 102, 107 and 112 correspond to an example of thedifferential metric computing section, the integration sections 125, 127and 129 correspond to an example of the first integration section, thepattern count sections 124, 126 and 128 correspond to an example fo thefirst count section, the level decision sections 103, 108 and 113correspond to an example of the differential metric extraction section,the integration sections 105, 110 and 115 correspond to an example ofthe second integration section, the pattern count sections 104, 109 and114 correspond to an example of the second count section, the errorcomputing sections 116, 117 and 118 correspond to an example of theerror rate computing section, and the standard deviation computingsection 120 corresponds to an example of the standard deviationcomputing section.

Now the computation of the standard deviation of the differentialmetrics for computing the predicted error rate according to the sixthembodiment will be described. The structure proposed by the third andfourth embodiments is where a mean value of the differential metrics isdetermined when the mean value of the outputs from the differentialmetric computing section does not match with the code distance of theideal signal depending on the recording state (quality), and a predictederror rate is computed from the standard deviation of the differentialmetric that is determined based on this mean value, so as to improve thecorrelation of an actually generated error rate and the signal indexvalue.

With this configuration of the third and fourth embodiments, however,two problems can occur. One, since the mean value of the differentialmetrics in the signal index value measurement area is determined, themean value must be determined in advance. In order to compute the signalindex value, a plurality of times of measurements are required, and thisprocessing may take time. Two, is that a possible solution for the firstproblem is to update the mean value while computing the mean value.However, the optimum value of the response characteristic of computingthe mean value could differ in some cases according to the recordingstate. Therefore it is difficult to unequivocally determine the responsecharacteristic of computing the mean value for obtaining compatiblesignal index values.

In order to solve these problems, the sixth embodiment suggests acalculation method that uses a predetermined fixed value called the“code distance of an ideal signal” as the signal processing threshold inthe processing for determining a standard deviation from the output ofthe differential metric, just like the first embodiment. Then if themean value of the outputs of the differential metric computing sections102, 107 and 112 does not match with the code distance of the idealsignal depending on the recording state (quality), an error of thestandard deviation that is generated by a shift of the mean value iscorrected so that the insufficient correlation of the signal index valueand the bit error rate is solved.

Now a method for calculating the standard deviation according to thesixth embodiment will be described. First the parameters to be used forthe calculation in the sixth embodiment are redefined. S_(x) is anintegration value of the differential metrics, N_(x) is a frequency ofthe differential metrics (count value to indicate a number of times ofintegration of S_(x)), JS_(x) is an integration value of thedifferential metrics which are not greater than the signal processingthreshold (“0” in this case), JN_(x) is a frequency of the differentialmetrics which are not greater than a signal processing threshold (“0” inthis case) (count value to indicate a number of times of integration ofJS_(x)), α is a predetermined frequency coefficient, and E_(x) is anideal signal processing value.

FIG. 20A and FIG. 20B are diagrams depicting the calculation method forthe standard deviation according to the sixth embodiment. In order todetermine a virtual standard deviation σ_(x)′ from the integration valueJS_(x) indicated by the hatched portion shown in FIG. 20A, theintegration value JS_(x) must be normalized by the count value N_(x). xmeans to determine a virtual standard deviation for the pattern groupsin Tables 1, 2 and 3 respectively. One of the values “1”, “2” and “3”corresponding to Table 1, Table 2 and Table 3 is inserted in x. E_(x)indicates a detection window, and “14” is inserted for the pattern groupin Table 1, and “12” is inserted for the pattern groups in Table 2 andTable 3.

The count value N_(x) can be determined by the following Expression(43).

$\begin{matrix}{N_{x} = {\frac{\alpha}{\sqrt{2\pi}\sigma_{x}^{\prime}}{\int_{- E_{x}}^{E_{x}}{^{\frac{- {({x - \mu})}^{2}}{2\sigma_{x}^{\prime 2}}}{x}}}}} & ({E43})\end{matrix}$

The count value JN_(x) can be determined by the following Expression(44).

$\begin{matrix}{{JN}_{x} = {\frac{\alpha}{\sqrt{2\; \pi}\sigma_{x}^{\prime}}{\int_{- E_{x}}^{0}{^{\frac{- {({x - \mu})}^{2}}{2\sigma_{x}^{\prime \; 2}}}{x}}}}} & ({E44})\end{matrix}$

The mean value μ of the distributions of Expression (43) and Expression(44) is defined by the following Expression (45).

$\begin{matrix}{\mu = \frac{S_{x}}{N_{x}}} & ({E45})\end{matrix}$

The calculation for normalizing the integration value JS_(x) by thecount value N_(x) is given by the following Expression (46).

$\begin{matrix}\begin{matrix}{\frac{{JS}_{x}}{N_{x}} = \frac{\frac{\alpha}{\sqrt{2\pi}\sigma_{x}^{\prime}}{\int_{- E_{x}}^{0}{{- x} \times ^{\frac{- {({x - \mu})}^{2}}{2\sigma_{x}^{\prime 2}}}{x}}}}{\frac{\alpha}{\sqrt{2\; \pi}\sigma_{x}^{\prime}}{\int_{- E_{x}}^{E_{x}}{^{\frac{- {({x - \mu})}^{2}}{2\sigma_{x}^{\prime 2}}}{x}}}}} \\{= \frac{\frac{\alpha}{\sqrt{2\pi}\sigma_{x}^{\prime}}{\int_{{- E_{x}} - \mu}^{- \mu}{{- \left( {x^{\prime} + \mu} \right)} \times ^{\frac{- x^{\prime 2}}{2\sigma_{x}^{\prime 2}}}{x^{\prime}}}}}{\frac{\alpha}{\sqrt{2\pi}\sigma_{x}^{\prime}}{\int_{{- E_{x}} - \mu}^{E_{x} - \mu}{^{\frac{- x^{\prime \; 2}}{2\sigma_{x}^{\prime \; 2}}}{x^{\prime}}}}}} \\{\cong {{\frac{\sigma_{x}^{\prime}}{\sqrt{2\; \pi}}^{- \frac{\mu^{2}}{2\sigma_{x}^{\prime 2}}}} - \frac{S_{x}{JN}_{x}}{N_{x}^{2}}}}\end{matrix} & ({E46})\end{matrix}$

In other words, the above Expression (46) can be transformed into thefollowing Expression (47).

$\begin{matrix}{{\left( {\frac{{JS}_{x}}{N_{x}E_{x}} - \frac{S_{x}{JN}_{x}}{E_{x}N_{x}^{2}}} \right)\sqrt{2\pi}} = {\left( \frac{\sigma_{x}^{\prime}}{E_{x}} \right)^{- \frac{{(\frac{S_{x}}{N_{x}E_{x}})}^{2}}{2{(\frac{\sigma_{x}^{\prime}}{E_{x}})}^{2}}}}} & ({E47})\end{matrix}$

Using two variables, a_(x) given by the following Expression (48) andb_(x) given by the following Expression (49), Expression (47) can besimplified into the following Expression (50).

$\begin{matrix}{{\left( {\frac{{JS}_{x}}{N_{x}E_{x}} - \frac{S_{x}{JN}_{x}}{E_{x}N_{x}^{2}}} \right)\sqrt{2\pi}} = a_{x}} & ({E48}) \\{\frac{S_{x}}{N_{x}E_{x}} = b_{x}} & ({E49}) \\{a_{x} = {\left( \frac{\sigma_{x}^{\prime}}{E_{x}} \right)^{- \frac{{(b_{x})}^{2}}{2{(\frac{\sigma_{x}^{\prime}}{E_{x}})}^{2}}}}} & ({E50})\end{matrix}$

A conversion table for determining the standard deviation a_(x)considering the shift of the mean value of the distribution based ona_(x) given by Expression (48) and the variable b_(x) given byExpression (49) (FIG. 20B) will be described next.

The above Expression (50), which is defined as a function of whicharguments are the standard deviation σ_(x) and variable b_(x), can begiven by the following Expression (51).

a _(x) =W(σ_(x) ,b _(x))  (E 51)

If the virtual standard deviation σ_(x)′ shown in FIG. 20A is moved tothe left side of the Expression (51), the following Expression (52) isestablished.

$\begin{matrix}{\frac{\sigma_{x}^{\prime}}{E_{x}} = {{W^{- 1}\left( {a_{x},b_{x}} \right)} \cong {{{P^{\prime}\left( b_{x} \right)}a_{x}} + {Q^{\prime}\left( b_{x} \right)}}}} & ({E52})\end{matrix}$

The index in which the shift of the mean value of the distribution shownin FIG. 20B is reflected to the detection window can be defined as thefollowing Expression (53).

$\begin{matrix}\begin{matrix}{\frac{\sigma_{x}}{2} = \frac{\sigma_{x}^{\prime}}{2\left( {1 + \frac{\mu}{E_{x}}} \right)}} \\{= \frac{\sigma_{x}^{\prime}}{2\left( {1 + b_{x}} \right)E_{x}}} \\{= \frac{{{P^{\prime}\left( b_{x} \right)}a_{x}} + {Q^{\prime}\left( b_{x} \right)}}{2\left( {1 + b_{x}} \right)}} \\{= {{{P\left( b_{x} \right)}a_{x}} + {Q\left( b_{x} \right)}}}\end{matrix} & ({E53})\end{matrix}$

σ_(x)/2 for the two variables, a_(x) and b_(x), that satisfy the aboveExpression (53), is calculated by the Newton's method. The Newton'smethod is one of the algorithms for solving equations based on iterationwhich is used for solving equations by numerical calculation in thenumerical analysis field, and has been used for numerical calculationfor a long time. Here descriptions on algorithm of the Newton's methodare omitted.

As described in FIG. 18, the shift amount of the mean value of theoutputs of the differential metrics is a variable b_(x) that isdetermined by the above Expression (49). The relationship of σ_(x)/2 [%]and the variable a_(x) shown in FIG. 18 can be expressed by a linearexpression of which variable is b_(x). Hence σ_(x)/2 which is determinedby the Newton's method can be expressed by the linear expression ofwhich variable b_(x) is the mean value of the outputs of thedifferential metrics.

In the linear expression of the above Expression (53), P denotes aninclination when the mean value of the outputs of the differentialmetrics is the variable b_(x), and Q denotes an intercept when the meanvalue of the outputs of the differential metrics is the variable b_(x).The value of P and the value of Q may be stored in the table withrespect to b_(x) determined by approximate calculation. In other words,the standard deviation computing section 120 may be stored in a table inadvance, which indicates the value of P and the value of Q of whichargument is the variable b_(x), shown in Table 4.

In this way, according to the sixth embodiment, correction computing fordetermining the standard deviation σ_(x)/2 considering the shift of themean value of the distribution is performed using the integration value(JS_(x)) and a number of times of integration (JN_(x)) based on theshift amount (S_(x)/N_(x)) of the distribution of the outputs of thedifferential metric computing sections 102, 107 and 112 and the fixedsignal processing threshold. In the sixth embodiment, a simple linearexpression given by the Expression (53) is used for the correctionexpression to improve the predicted error rate computing accuracy.

In the sixth embodiment, the predicted error rate is determined usingthe standard deviation σ_(x)/2 that is determined by the Expression (53)according to the pattern groups in Table 1 to Table 3. Thereby a signalindex value which has a high correlation with the error rate can bedetermined even if the center of the distribution of the outputs of thedifferential metric computing sections 102, 107 or 112 is shifted fromthe signal processing threshold as in FIG. 21B and FIG. 21C.

The above embodiments mainly include the invention having the followingstructures.

The reproduction signal evaluation method according to an aspect of thepresent invention is a reproduction signal evaluation method forevaluating a quality of a reproduction signal reproduced from aninformation recording medium based on a binary signal generated from thereproduction signal using a PRML signal processing system, the methodcomprising: a pattern extraction step of extracting, from the binarysignal, a specific state transition pattern which has a possibility ofcausing a bit error; a differential metric computing step of computing adifferential metric, which is a difference of a first metric between anideal signal of a most likely first state transition sequencecorresponding to the binary signal and the reproduction signal, and asecond metric between an ideal signal of a second most likely secondstate transition sequence corresponding to the binary signal and thereproduction signal, based on the binary signal of the state transitionpattern extracted in the pattern extraction step; a first integrationstep of integrating the differential metric computed in the differentialmetric computing step; a first count step of counting a number of timesof integration processing in the first integration step; a differentialmetric extraction step of extracting the differential metric not greaterthan a predetermined signal processing threshold; a second integrationstep of integrating the differential metric not greater than the signalprocessing threshold extracted in the differential metric extractionstep; a second count step of counting a number of times of integrationprocessing in the second integration step; an error rate computing stepof computing an error rate predicted based on an integration value thatis integrated in the first integration step, a count value that iscounted in the first count step, an integration value that is integratedin the second integration step, and a count value that is counted in thesecond count step; a standard deviation computing step of computing astandard deviation based on the error rate that is computed in the errorrate computing step; and an evaluation step of evaluating a quality ofthe reproduction signal using the standard deviation computed in thestandard deviation computing step.

According to the foregoing structure, specific state transition patternswhich have the possibility of causing a bit error are extracted from thebinary signals generated by reproducing the information recordingmedium. Here the state transition pattern, which has a possibility ofcausing a bit error, is a state transition pattern having merging pathswhich could take a plurality of state transitions when a predeterminedstate at a certain time transits to a predetermined state at anothertime, and is a state transition pattern of merging paths of whichEuclidean distance between an ideal signal of a most likely first statetransition sequence and an ideal signal of a second most likely secondstate transition sequence is relatively short. If there are a pluralityof state transition patterns which have the possibility of causing a biterror, a specific state transition pattern is selectively extracted.

Targeting the binary signal of the extracted specific state transitionpattern, a differential metric, which is a difference of a first metricbetween an ideal signal of a most likely first state transition sequencecorresponding to this binary signal and the reduction signal, and asecond metric between an ideal signal of a second most likely secondstate transition sequence corresponding to this binary signal and thereproduction signal, is computed.

Then the calculated differential metric is integrated, and a number oftimes of integration processing of the differential metrics is counted.Differential metrics which are not greater than a predetermined signalprocessing threshold are extracted, and the extracted differentialmetrics which are not greater than the signal processing threshold areintegrated, and a number of times of integration processing ofdifferential metrics which are not greater than the signal processingthreshold are counted.

Then an error rate predicted based on the computed integration value ofthe differential metrics, the count value of a number of times ofintegration processing of the differential metrics, and the integrationvalue of the differential metrics which are not greater than thepredetermined signal processing threshold and the count value of anumber of times of integration processing of the differential metricswhich are not greater than the predetermined signal processing thresholdare computed. Further the standard deviation is computed based on thecomputed error rate, and the quality of the reproduction signal isevaluated using the computed standard deviation.

Therefore when the mean value of the differential metrics does not matchwith the code distance of the ideal signal depending on the recordingstate, an error of the standard deviation, which is generated by a shiftof the mean value of the differential metrics from the code distance ofthe ideal signal, is corrected using the computed integration value ofthe differential metrics, and the count value of a number of times ofintegration processing of the differential metrics, whereby correlationof the error rate and the signal index value is improved and the qualityof the reproduction signal reproduced from the information recordingmedium, can be evaluated at high accuracy.

In the foregoing reproduction signal evaluation method, it is preferablethat the signal processing threshold is a square of a Euclidian distancebetween the ideal signal of the most likely first state transitionsequence and the ideal signal of the second most likely second statetransition sequence.

According to the foregoing structure, the signal processing threshold,corresponding to the extraction target specific state transitionpatterns, can be accurately set so as to match with the Euclidiandistance between the ideal signal of the first state transition sequenceand the ideal signal of the second state transition sequence. This isparticularly effective to evaluate signals where a plurality of statetransition patterns, which have the possibility of generating an error,are mixed.

In the foregoing reproduction signal evaluation method, it is preferablethat the error rate computing step computes a standard deviation ofdifferential metrics not greater than a mean value of differentialmetric outputs, using a linear expression of which arguments are theintegration value that is integrated in the first integration step, thecount value that is counted in the first count step, the integrationvalue that is integrated in the second integration step, and the countvalue that is counted in the second count step, and computes the errorrate based on the standard deviation.

According to the foregoing structure, the standard deviation of thedifferential metrics not greater than the mean value of the differentialmetric outputs is computed using a linear expression of which argumentsare the computed integration value of the differential metrics, thecount value of a number of times of integration processing of thedifferential metrics, the integration value of the differential metricswhich are not greater than the predetermined signal threshold and thecount value of a number of times of integration processing of thedifferential metrics which are not greater than the predetermined signalprocessing threshold, and the error rate can be computed based on thestandard deviation.

In the foregoing reproduction signal evaluation method, it is preferablethat the linear expression is an approximate expression that iscalculated using iteration based on the Newton's method. According tothe foregoing structure, the linear expression used for computing theerror rate can be given by an approximate expression that is computedusing iteration based on the Newton's method.

In the foregoing reproduction signal evaluation method, it is preferablethat the error rate computing step computes the error rate based on themean value of the differential metrics computed based on the integrationvalue that is integrated in the first integration step and the countvalue that is counted in the first count step, and a predeterminedcomputing result based on the integration value that is integrated inthe second integration step and the count value that is counted in thesecond count step.

According to the foregoing structure, the error rate can be computedbased on the mean value of the differential metrics computed based onthe computed integration value of the differential metric and the countvalue of a number of times of integration processing of differentialmetrics, and a predetermined computing result based on the integrationvalue of the differential metrics which are not greater than thepredetermined signal processing threshold and the count value of anumber of times of integration processing of the differential metricswhich are not greater than the predetermined signal processingthreshold.

The reproduction signal evaluation method according to another aspect ofthe present invention is a reproduction signal evaluation method forevaluating a quality of a reproduction signal reproduced from aninformation recording medium based on a binary signal generated from thereproduction signal using a PRML signal processing system, the methodcomprising: a pattern extraction step of extracting, from the binarysignal, a plurality of state transition patterns which have apossibility of causing a bit error; a differential metric computing stepof computing a differential metric, which is a difference of a firstmetric between an ideal signal of a most likely first state transitionsequence corresponding to the binary signal and the reproduction signal,and a second metric between an ideal signal of a second most likelysecond state transition sequence corresponding to the binary signal andthe reproduction signal, based on the binary signal for each statetransition pattern extracted in the pattern extraction step; a firstintegration step of integrating the differential metrics computed in thedifferential metric computing step for each of the state transitionpatterns respectively; a first count step of counting a number of timesof integration processing in the first integration step for each of thestate transition patterns; a differential metric extraction step ofextracting the differential metric not greater than a predeterminedsignal processing threshold for each of the state transition patternsrespectively; a second integration step of integrating the differentialmetric not greater than the signal processing threshold extracted in thedifferential metric extraction step for each of the state transitionpatterns respectively; a second count step of counting a number of timesof integration processing in the second integration step for each of thestate transition patterns; an error rate computing step of computing,for each of the state transition patterns, a plurality of error ratespredicted based on the plurality of integration values that areintegrated in the first integration step, the plurality of count valuesthat are counted in the first count step, the plurality of integrationvalues that are integrated in the second integration step, and theplurality of count values that are counted in the second count step; astandard deviation computing step of computing a standard deviationbased on the total of the plurality of error rates that are computed inthe error rate computing step; and an evaluation step of evaluating aquality of the reproduction signal, using the standard deviationcomputed in the standard deviation computing step.

According to the foregoing structure, a plurality of state transitionpatterns, which have the possibility of causing a bit error, areextracted from the binary signal generated by reproducing theinformation recording medium. And based on the binary signal, thedifferential metric, which is a difference of a first metric between anideal signal of a most likely first state transition sequencecorresponding to the binary signal and the reproduction signal, and asecond metric between an ideal signal of a second most likely secondstate transition sequence corresponding to this binary signal and thereproduction signal, is computed for each of the extracted statetransition patterns respectively.

Then the calculated differential metric is integrated for each statetransition pattern, and a number of times of integration processing ofthe differential metrics is counted for each state transition patternrespectively. The differential metrics, which are not greater than apredetermined signal processing threshold, are extracted for each statetransition pattern, the extracted differential metrics, which are notgreater than the signal processing threshold, are integrated for eachstate transition pattern, and a number of integration processing of thedifferential metric, which are not greater than the signal processingthreshold, is counted for each state transition pattern.

Then a plurality of error rates predicted based on the plurality ofcomputed integration values of the differential metrics, the pluralityof count values of a number of times of integration processing of thedifferential metrics, the plurality of integration values of thedifferential metrics which are not greater than the predetermined signalprocessing threshold and the plurality of count values of a number oftimes of integration processing of differential metrics which are notgreater than the predetermined signal processing threshold, are computedfor each state transition pattern. Further the standard deviation iscomputed based on the total of the computed plurality of error rates,and the quality of the reproduction signal is evaluated using thecomputed standard deviation.

Therefore when the mean value of the differential metrics does not matchwith the code distance of the ideal signal depending on the recordingstate, an error of the standard deviation, which is generated by a shiftof the mean value of the differential metrics from the code distance ofthe ideal signal, is corrected using the computed integration value ofthe differential metrics and the count value of a number of times ofintegration processing of the differential metrics, whereby correlationof the error rate and the signal index value is improved, and thequality of the reproduction signal reproduced from the informationrecording medium can be evaluated at high accuracy.

The reproduction signal evaluation unit according to another aspect ofthe present invention is a reproduction signal evaluation unit forevaluating a quality of a reproduction signal reproduced from aninformation recording medium based on a binary signal generated from thereproduction signal using a PRML signal processing system, the unitcomprising: a pattern extraction section for extracting, from the binarysignal, a specific state transition pattern which has a possibility ofcausing a bit error; a differential metric computing section forcomputing a differential metric, which is a difference of a first metricbetween an ideal signal of a most likely first state transition sequencecorresponding to the binary signal and the reproduction signal, and asecond metric between an ideal signal of a second most likely secondstate transition sequence corresponding to the binary signal and thereproduction signal, based on the binary signal of the state transitionpattern extracted by the pattern extraction section; a first integrationsection for integrating the differential metric computed by thedifferential metric computing section; a first count section forcounting a number of times of integration processing by the firstintegration section; a differential metric extraction section forextracting the differential metric not greater than a predeterminedsignal processing threshold; a second integration section forintegrating the differential metric not greater than the signalprocessing threshold extracted by the differential metric extractionsection; a second count section for counting a number of times ofintegration processing by the second integration section; an error ratecomputing section for computing an error rate predicted based on theintegration value that is integrated by the first integration section,the count value that is counted by the first count section, theintegration value that is integrated by the second integration section,and the count value that is counted by the second count section; and astandard deviation computing section for computing a standard deviationbased on the error rate that is computed by the error rate computingsection.

According to the foregoing structure, specific state transition patternswhich have the possibility of causing a bit error are extracted from thebinary signals generated by reproducing the information recordingmedium. Here the state transition pattern, which has a possibility ofcausing a bit error, is a state transition pattern having merging pathswhich could take a plurality of state transitions when a predeterminedstate at a certain time transits to a predetermined state at anothertime, and is a state transition pattern of merging paths of whichEuclidean distance between an ideal signal of a most likely first statetransition sequence and an ideal signal of a second most likely secondstate transition sequence is relatively short. If there are a pluralityof state transition patterns which have the possibility of causing a biterror, a specific state transition pattern is selectively extracted.

Targeting the binary signal of the extracted specific state transitionpattern, a differential metric, which is a difference of a first metricbetween an ideal signal of a most likely first state transition sequencecorresponding to this binary signal and the reproduction signal, and asecond metric between an ideal signal of a second most likely secondstate transition sequence corresponding to this binary signal and thereproduction signal, is computed.

Then the calculated differential metrics are integrated, and a number oftimes of integration processing of the differential metrics are counted.Differential metrics which are not greater than a predetermined signalprocessing threshold are extracted, and the extracted differentialmetrics which are not greater than the signal processing threshold areintegrated, and a number of times of integration processing of thedifferential metrics which are not greater than the signal processingthreshold is counted.

Then an error rate, which is predicted based on the computed integrationvalue of the differential metrics, the count value of a number of timesof integration processing of the differential metrics, and theintegration value of the differential metrics which are not greater thanthe predetermined signal processing threshold and the count value of anumber of times of integration processing of the differential metricswhich are not greater than the predetermined signal processingthreshold, is computed. Further the standard deviation is computed basedon the computed error rate, and the quality of the reproduction signalis evaluated using the computed standard deviation.

Therefore when the mean value of the differential metrics does not matchwith the code distance of the ideal signal, an error of the standarddeviation, which is generated by a shift of the mean value of thedifferential metrics from the code distance of the ideal signal, iscorrected using the computed integration value of the differentialmetrics, and the count value of a number of times of integrationprocessing of the differential metrics, whereby correlation of the errorrate and the signal index value is improved and the quality of thereproduction signal reproduced from the information recording medium,can be evaluated at high accuracy.

In the foregoing reproduction signal evaluation unit, it is preferablethat the signal processing threshold is a square of a Euclidean distancebetween the ideal signal of the most likely first state transitionsequence and the ideal signal of the second most likely second statetransition sequence.

According to the foregoing structure, the signal processing threshold,corresponding to the extraction target specific state transitionpattern, can be accurately set so as to match with the Euclidiandistance between the ideal signal of the first state transition sequenceand the ideal signal of the second state transition sequence. This isparticularly effective to evaluate signals where a plurality of statetransition patterns, which have the possibility of generating an error,are mixed.

In the foregoing reproduction signal evaluation unit, it is preferablethat the error rate computing section computes a standard deviation ofdifferential metrics not greater than a mean value of differentialmetric outputs, using a linear expression of which arguments are theintegration value that is integrated by the first integration section,the count value that is counted by the first count section, theintegration value that is integrated by the second integration section,and the count value that is counted by the second count section, andcomputes the error rate based on the standard deviation.

According to the foregoing structure, the standard deviation of thedifferential metrics not greater than the mean value of the differentialmetric outputs is computed using a linear expression of which argumentsare the computed integration value of the differential metrics, thecount value of a number of times of integration processing of thedifferential metrics, the integration value of the differential metricswhich are not greater than the predetermined signal processing thresholdand the count value of a number of times of integration processing ofthe differential metrics which are not greater than the predeterminedsignal processing threshold differential metric, and the error rate canbe computed based on the standard deviation.

In the foregoing reproduction signal evaluation unit, it is preferablethat the linear expression is an approximate expression that iscalculated using iteration based on the Newton's method. According tothe foregoing structure, the linear expression used for computing theerror rate can be given by an approximate expression that is computedand using iteration based on the Newton's method.

In the foregoing reproduction signal evaluation unit, it is preferablethat the error rate computing section computes the error rate based onthe mean value of the differential metrics computed based on theintegration value that is integrated by the first integration sectionand the count value that is counted by the first count section, and apredetermined computing result based on the integration value that isintegrated by the second integration section and the count value that iscounted by the second count section.

According to the foregoing structure, the error rate can be computedbased on the mean value of the differential metrics computed based onthe computed integration value of the differential metric and the countvalue of a number of times of integration processing of differentialmetrics, and a predetermined computing result based on the integrationvalue of the differential metrics which are not greater than thepredetermined signal processing threshold and the count value of anumber of times of integration processing of the differential metricswhich are not greater than the predetermined signal processingthreshold.

The reproduction signal evaluation unit according to another aspect ofthe present invention is a reproduction signal evaluation unit forevaluating the quality of a reproduction signal reproduced from aninformation recording medium based on a binary signal generated from thereproduction signal using a PRML signal processing system, the unitcomprising: a pattern extraction section for extracting, from the binarysignal, a plurality of state transition patterns which have apossibility of causing a bit error; a differential metric computingsection for computing a differential metric, which is a difference of afirst metric between an ideal signal of a most likely first statetransition sequence corresponding to the binary signal and thereproduction signal, and a second metric between an ideal signal of asecond most likely second state transition sequence corresponding to thebinary signal and the reproduction signal, based on the binary signalfor each state transition pattern extracted by the pattern extractionsection; a first integration section for integrating the differentialmetric computed by the differential metric computing section for each ofthe state transition patterns; a first count section for counting anumber of times of integration processing by the first integrationsection for each of the state transition patterns; a differential metricextraction section for extracting the differential metric not greaterthan a predetermined signal processing threshold for each of the statetransition patterns; a second integration section for integrating thedifferential metric not greater than the signal processing thresholdextracted by the differential metric extraction section for each of thestate transition patterns; a second count section for counting a numberof times of integration processing by the second integration section foreach of the state transition patterns; an error rate computing sectionfor computing, for each of the state transition patterns, a plurality oferror rates predicted based on the plurality of integration values thatare integrated by the first integration section, the plurality of countvalues that are counted by the first count section, the plurality ofintegration values that are integrated by the second integrationsection, and the plurality of count values that are counted by thesecond count section; and a standard deviation computing section forcomputing a standard deviation based on the total of the plurality oferror rates that are computed by the error rate computing section.

According to the foregoing structure, a plurality of state transitionpatterns, which have the possibility of causing a bit error, areextracted from the binary signal generated by reproducing theinformation recording medium. And based on the binary signal, thedifferential metric, which is a difference of a first metric between anideal signal of a most likely first state transition sequencecorresponding to the binary signal and the reproduction signal, and asecond metric between an ideal signal of a second most likely secondstate transition sequence corresponding to this binary signal and thereproduction signal, is computed for each of the extracted statetransition patterns respectively.

Then the calculated differential metrics are integrated for each statetransition pattern, and a number of times of integration processing ofthe differential metric is counted for each state transition patternrespectively. Differential metrics, which are not greater than apredetermined signal processing threshold, are extracted for each statetransition pattern, the extracted differential metrics, which are notgreater than the signal processing threshold, are integrated for eachstate transition pattern, and a number of integration processing of thedifferential metric, which are not greater than the signal processingthreshold, is counted for each state transition pattern.

Then a plurality of error rates predicted based on the plurality ofcomputed integration values of the differential metrics, the pluralityof count values of a number of times of integration processing of thedifferential metrics, the plurality of integration values of thedifferential metrics which are not greater than the predetermined signalprocessing threshold and the plurality of count values of a number oftimes of integration processing of the differential metrics which arenot greater than the predetermined signal processing threshold, arecomputed for each state transition pattern. Further the standarddeviation is computed based on the total of the computed plurality oferror rates, and the quality of the reproduction signal is evaluatedusing the computed standard deviation.

Therefore when the mean value of the differential metrics does not matchwith the code distance of the ideal signal depending on the recordingstate, an error of the standard deviation, which is generated by a shiftof the mean value of the differential metrics from the code distance ofthe ideal signal, is corrected using the computed integration value ofthe differential metrics and the count value of a number of times ofintegration processing of the differential metrics, whereby correlationof the error rate and the signal index value is improved, and thequality of the reproduction signal reproduced from the informationrecording medium can be evaluated at high accuracy.

An optical disk device according to another aspect of the presentinvention, comprises: a reproduction section for generating a binarysignal from a reproduction signal reproduced from an optical disk, whichis an information recording medium, using a PRML signal processingsystem; and the preproduction signal evaluation unit according to one ofthe above descriptions. According to this structure, the foregoingreproduction signal evaluation unit can be applied to the optical diskdevice.

Specific embodiments or examples used for the description of thepreferred embodiments are merely to clarify the technical content of thepresent invention, and the present invention should not be interpretedwithin these limited examples, but can be modified in various wayswithin the spirit of the present invention and scope of the Claims.

This application is based on U.S. Provisional Application No. 61/149,584filed on Feb. 3, 2009, the contents of which are hereby incorporated byreference.

Specific embodiments or examples for the detailed description of theinvention are merely to clarify the technical content of the presentinvention, and the present invention should not be interpreted withinthese limited examples, but can be modified in various ways within thespirit of the present invention and scope of the Claims described hereinbelow.

1. A reproduction signal evaluation method for evaluating a quality of areproduction signal reproduced from an information recording mediumbased on a binary signal generated from the reproduction signal using aPRML signal processing system, the method comprising: a patternextraction step of extracting, from the binary signal, a specific statetransition pattern which has a possibility of causing a bit error; adifferential metric computing step of computing a differential metric,which is a difference of a first metric between an ideal signal of amost likely first state transition sequence corresponding to the binarysignal and the reproduction signal, and a second metric between an idealsignal of a second most likely second state transition sequencecorresponding to the binary signal and the reproduction signal, based onthe binary signal of the state transition pattern extracted in thepattern extraction step; a first integration step of integrating thedifferential metric computed in the differential metric computing step;a first count step of counting a number of times of integrationprocessing in the first integration step; a differential metricextraction step of extracting the differential metric not greater than apredetermined signal processing threshold; a second integration step ofintegrating the differential metric not greater than the signalprocessing threshold extracted in the differential metric extractionstep; a second count step of counting a number of times of integrationprocessing in the second integration step; an error rate computing stepof computing an error rate predicted based on an integration value thatis integrated in the first integration step, a count value that iscounted in the first count step, an integration value that is integratedin the second integration step, and a count value that is counted in thesecond count step; a standard deviation computing step of computing astandard deviation based on the error rate that is computed in the errorrate computing step; and an evaluation step of evaluating a quality ofthe reproduction signal using the standard deviation computed in thestandard deviation computing step.
 2. The reproduction signal evaluationmethod according to claim 1, wherein the signal processing threshold isa square of a Euclidian distance between the ideal signal of the mostlikely first state transition sequence and the ideal signal of thesecond most likely second state transition sequence.
 3. The reproductionsignal evaluation method according to claim 1, wherein the error ratecomputing step computes a standard deviation of differential metrics notgreater than a mean value of differential metric outputs, using a linearexpression of which arguments are the integration value that isintegrated in the first integration step, the count value that iscounted in the first count step, the integration value that isintegrated in the second integration step, and the count value that iscounted in the second count step, and computes the error rate based onthe standard deviation.
 4. The reproduction signal evaluation methodaccording to claim 3, wherein the linear expression is an approximateexpression that is calculated using iteration based on the Newton'smethod.
 5. The reproduction signal evaluation method according to claim1, wherein the error rate computing step computes the error rate basedon the mean value of the differential metrics computed based on theintegration value that is integrated in the first integration step andthe count value that is counted in the first count step, and apredetermined computing result based on the integration value that isintegrated in the second integration step and the count value that iscounted in the second count step.
 6. A reproduction signal evaluationmethod for evaluating a quality of a reproduction signal reproduced froman information recording medium based on a binary signal generated fromthe reproduction signal using a PRML signal processing system, themethod comprising: a pattern extraction step of extracting, from thebinary signal, a plurality of state transition patterns which have apossibility of causing a bit error; a differential metric computing stepof computing a differential metric, which is a difference of a firstmetric between an ideal signal of a most likely first state transitionsequence corresponding to the binary signal and the reproduction signal,and a second metric between an ideal signal of a second most likelysecond state transition sequence corresponding to the binary signal andthe reproduction signal, based on the binary signal for each statetransition pattern extracted in the pattern extraction step; a firstintegration step of integrating the differential metrics computed in thedifferential metric computing step for each of the state transitionpatterns respectively; a first count step of counting a number of timesof integration processing in the first integration step for each of thestate transition patterns; a differential metric extraction step ofextracting the differential metrics not greater than a predeterminedsignal processing threshold for each of the state transition patternsrespectively; a second integration step of integrating the differentialmetrics not greater than the signal processing threshold extracted inthe differential metric extraction step for each of the state transitionpatterns respectively; a second count step of counting a number of timesof integration processing in the second integration step for each of thestate transition patterns; an error rate computing step of computing,for each of the state transition patterns, a plurality of error ratespredicted based on the plurality of integration values that areintegrated in the first integration step, the plurality of count valuesthat are counted in the first count step, the plurality of integrationvalues that are integrated in the second integration step, and theplurality of count values that are counted in the second count step; astandard deviation computing step of computing a standard deviationbased on the total of the plurality of error rates that are computed inthe error rate computing step; and an evaluation step of evaluating aquality of the reproduction signal, using the standard deviationcomputed in the standard deviation computing step.
 7. A reproductionsignal evaluation unit for evaluating a quality of a reproduction signalreproduced from an information recording medium based on a binary signalgenerated from the reproduction signal using a PRML signal processingsystem, the unit comprising: a pattern extraction section forextracting, from the binary signal, a specific state transition patternwhich has a possibility of causing a bit error; a differential metriccomputing section for computing a differential metric, which is adifference of a first metric between an ideal signal of a most likelyfirst state transition sequence corresponding to the binary signal andthe reproduction signal, and a second metric between an ideal signal ofa second most likely second state transition sequence corresponding tothe binary signal and the reproduction signal, based on the binarysignal of the state transition pattern extracted by the patternextraction section; a first integration section for integrating thedifferential metric computed by the differential metric computingsection; a first count section for counting a number of times ofintegration processing by the first integration section; a differentialmetric extraction section for extracting the differential metric notgreater than a predetermined signal processing threshold; a secondintegration section for integrating the differential metric not greaterthan the signal processing threshold extracted by the differentialmetric extraction section; a second count section for counting a numberof times of integration processing by the second integration section; anerror rate computing section for computing an error rate predicted basedon the integration value that is integrated by the first integrationsection, the count value that is counted by the first count section, theintegration value that is integrated by the second integration section,and the count value that is counted by the second count section; and astandard deviation computing section for computing a standard deviationbased on the error rate that is computed by the error rate computingsection.
 8. The reproduction signal evaluation unit according to claim7, wherein the signal processing threshold is a square of a Euclideandistance between the ideal signal of the most likely first statetransition sequence and the ideal signal of the second most likelysecond state transition sequence.
 9. The reproduction signal evaluationunit according to claim 7, wherein the error rate computing sectioncomputes a standard deviation of differential metric not greater than amean value of the differential metric outputs, using a linear expressionof which arguments are the integration value that is integrated by thefirst integration section, the count value that is counted by the firstcount section, the integration value that is integrated by the secondintegration section, and the count value that is counted by the secondcount section, and computes the error rate based on the standarddeviation.
 10. The reproduction signal evaluation unit according toclaim 9, wherein the linear expression is an approximate expression thatis calculated using iteration based on the Newton's method.
 11. Thereproduction signal evaluation unit according to claim 7, wherein theerror rate computing section computes the error rate based on the meanvalue of the differential metrics computed based on the integrationvalue that is integrated by the first integration section and the countvalue that is counted by the first count section, and a predeterminedcomputing result based on the integration value that is integrated bythe second integration section and the count value that is counted bythe second count section.
 12. A reproduction signal evaluation unit forevaluating a quality of a reproduction signal reproduced from aninformation recording medium based on a binary signal generated from thereproduction signal using a PRML signal processing system, the unitcomprising: a pattern extraction section for extracting, from the binarysignal, a plurality of state transition patterns which have apossibility of causing a bit error; a differential metric computingsection for computing a differential metric, which is a difference of afirst metric between an ideal signal of a most likely first statetransition sequence corresponding to the binary signal and thereproduction signal, and a second metric between an ideal signal of asecond most likely second state transition sequence corresponding to thebinary signal and the reproduction signal, based on the binary signalfor each state transition pattern extracted by the pattern extractionsection; a first integration section for integrating the differentialmetric computed by the differential metric computing section for each ofthe state transition patterns; a first count section for counting anumber of times of integration processing by the first integrationsection for each of the state transition patterns; a differential metricextraction section for extracting the differential metric not greaterthan a predetermined signal processing threshold for each of the statetransition patterns; a second integration section for integrating thedifferential metric not greater than the signal processing thresholdextracted by the differential metric extraction section for each of thestate transition patterns; a second count section for counting a numberof times of integration processing by the second integration section foreach of the state transition patterns; an error rate computing sectionfor computing, for each of the state transition patterns, a plurality oferror rates predicted based on the plurality of integration values thatare integrated by the first integration section, the plurality of countvalues that are counted by the first count section, the plurality ofintegration values that are integrated by the second integrationsection, and the plurality of count values that are counted by thesecond count section; and a standard deviation computing section forcomputing a standard deviation based on the total of the plurality oferror rates that are computed by the error rate computing section. 13.An optical disk device, comprising: a reproduction section forgenerating a binary signal from a reproduction signal reproduced from anoptical disk, which is an information recording medium, using a PRMLsignal processing system; and the preproduction signal evaluation unitaccording to claim 7.